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The Royal Palaces and sites
Click on a Palace or site to read more.
Welcome to one of Europe's largest and most dynamic palaces. The Royal Palace of Stockholm is His Majesty The King's official residence a...Read more
Drottningholm Palace is on UNESCO's World Heritage list. It is the most well-preserved royal castle built in the 1600s in Sweden and at t...Read more
On the shores of Lake Mälaren, Gripsholm Castle towers powerfully and fairytale-like over the idyllic small town of Mariefred in Söderman...Read more
The royal palaces have churches and chapels located in unique historical settings and in locations of great natural beauty. These churche...Read more
"He took me to the side of the pleasure gardens, and I was surprised to find myself suddenly standing in front of a real fairy tale palac...Read more
Ulriksdal Palace is situated on the banks of Edsviken Lake in the National City Park in Stockholm. The palace was built in the 1600s and ...Read more
At Rosersberg Palace, north of Stockholm, time has stood still. The rooms remain almost untouched from the 1795-1860 period, with well-pr...Read more
The Riddarholmen Church is the final resting place of the Swedish kings, and is Stockholm's only preserved mediaeval abbey. The church is...Read more
For more than a hundred years, the Royal Stables have been located at Väpnargatan 1 on Östermalm in Stockholm. The main duty of the Royal...Read more
The Royal Gift Shop is a unique present and souvenir shop, selling products with a connection to objects from the Royal Collections.Read more
The yellow Baroque Strömsholm Palace is situated near Västerås. The 18th century Gustavian décor and a significant collection of Swedish ...Read more
Tullgarn, near Trosa in Södermanland, is one of our most popular royal destinations and is less than an hour away from Stockholm by car. ...Read more
It was from here that King Gustav III set off for the fateful masquerade ball at the Royal Swedish Opera in March 1792. His pavilion in S...Read more
Rosendal Palace on Djurgården in Stockholm is a unique document of the Swedish Empire style, also known as the Karl Johan style. Rosendal...Read more
"I was glad that you could catch a glimpse of life on Djurgården, that you could see how the majesty of the oak trees depends on people p...Read more


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Story highlights
- Birth defects left Penny Loker with a disfigured face
- Growing up, she endured cruel treatment from others
- She wishes she could open the dialogue and educate others about malformations
- Loker wants to be a wife and mother, but accepts that may never happen
As a little girl, she never got a valentine at school parties.
As a teenager, she never had a best friend, a boyfriend or a date.
As a woman, she's never had a relationship. She's never been kissed.
Her dream to one day become a wife and mother is fading as she grapples with the reality that it may never happen.
Penny Loker, 31, was born with hemifacial microsomia and Goldenhar Syndrome, two birth defects that left her with a disfigured face. Hemifacial microsomia causes a malformation of the ear and/or the structure of the lower jaw. Goldenhar Syndrome is a congenital condition that produces abnormalities of the head and the bones of the spinal column. It usually affects the appearance of eyes, ears, facial bones and the mouth.
Both conditions are complex, as are their names. But for a little girl facing a big world, the names were simple: Monster. Ugly. Freak.
Loker was raised in Waterloo, Ontario, Canada, in a home where love and acceptance were given freely.
Her father died soon after Loker's first birthday. Her mother, a strong, loving woman, readily took on the challenge of raising Loker and her sisters. That included arranging for multiple surgeries at hospitals far from home.
Loker's recoveries were lengthy and painful, recalls older sister Crystal Loker. "But even then Penny had a positive attitude. She was a trouper who kept her smile and didn't complain."
At home, Loker's looks were not an issue and she was treated the same as her sisters. She knew she looked a bit different, and she knew it caused her to be in the hospital a lot, but she had a child's innocence and accepted it as normal.
When it was time to start school, she discovered how different she actually was. She spent a lonely childhood excluded from nearly everything. She had no friends. She accepted the name-calling as part of her life and seemed to understand that crying or fighting back only made it worse. So she quietly endured it.
She remembers that pain and humiliation well. "As an adult, it's hard to stand by silently when you know what these kids are going through," Loker says. "But it's equally difficult to speak out when you don't feel empowered to do so."
As she got older, the name-calling was more often behind her back than to her face. By the time she reached high school, she had learned there would be no friends, parties or dates for a girl who looked like a freak.
"It hurt, but there was nothing I could do to change it," she says.
While day-to-day encounters with strangers brought the same hurtful stares and comments, Loker was used to it. Then came a day with such cruelty it remains a vivid memory.
"For graduation my sister gave me a gift certificate for a manicure and pedicure. I'd never had one before, and it was a wonderful experience. I left the spa feeling truly pretty for the first time in my adult life."
But less than a block from the salon, she encountered a carload of boys who slowed to look at her and began pointing and calling her names.
Distraught, she walked as quickly as she could to get away from them, thinking all the while that she was not beautiful after all.
"The pretty nails made no difference at all. Shamed and humiliated, I realized I was still the same girl that everyone made fun of," she recalls. "I remember that day as one of the worst."
But time can ease all wounds, and her determined spirit taught her to find the good around her.
She has a core group of girlfriends and relatives who cherish her as she is. She speaks proudly of being able to provide for herself, thanks to her job at Rogers Communications, where she spends her days handling customer requests. The company was recognized this year as a diversity leader corporation, a recognition that only adds to Loker's pride.
There are many adventures in life that she is hesitant to embrace, even though she is physically able. She prefers not to shop alone. She has never been on a true vacation, saying the thought of one is scary.
Yet it's a feat she is determined to accomplish. She is saving now for a trip to California to visit either BlizzCon o

subset: commoncrawl


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AT&T offers lower-cost options on one- and two-line plans
SAN FRANCISCO — A little more than a month after unveiling lower-cost family wireless plans, AT&T on Saturday announced a price cut for customers with one or two lines under its Mobile Share Value program.
Beginning Sunday, AT&T will offer a plan with 2GB of data for $65 a month for one smartphone or $90 for two phones sharing the 2GB of data.
Each price is $15 a month lower than its current offering, AT&T said.
As with the family plans it announced in February, you can buy a phone on a payment plan or bring your own.
AT&T's pricing moves come at a time of big change in the industry as rivals tinker with consumer plan offerings. Rival T-Mobile has aggressively priced its offerings and is marketing itself as the "Uncarrier" — doing away with contracts and paying early termination fees if you switch to it network, among other things.
Tech industry analyst Jeff Kagan expects to see more moves by AT&T.
"AT&T continues to evolve," he said. "I don't think they are done yet."


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Six Wabash students are in Berlin, Germany, until May 20 as part of Professor John Byrne's German 202 class. Students on the trip are corresponding and sending back photos to document their immersion learning experience.
(Received 5/15/05) From Andy Deig:
Two days ago, we went to a concentration camp in Sachsenhausen. It was not actually an extermination camp, but a forced labor camp. It was very eye opening. Seeing the barracks, the infirmary, the place where executions took place. (See photo album from 5/14 at bottom of page.)
We found out from looking at a map in one of the musuems that there were actually concentration camps everywhere throughout Germany. There were so many that we couldn't count. There would be a large camp like the ones at Auschwitz or Sachsenhausen and there would be a ton of little camps around there.
Yesterday, we went to the Schloss Sans Sounci (which means the castle of no worries) which was the summer house for Friedrich the Great. There were also many other cool buildings and a beautiful summer garden that we enjoyed. (The photo above are the six Wabash students at one of the castles they visited 5/14.)
After the visit to the castle, we walked through Potsdam, which is an awesome city and enjoyed some of the food there.
Today we saw one of the coolest museums I've ever been to. It was das jüdisches Museum Berlin or the Jewish Museum of Berlin. It was constructed into 3 Axes. The Axis of the Holocaust which led us to a triangular room with huge concrete walls. Once the door to the room was shut, there was no way out. There was a ladder placed high enough on the wall that there is no way any one could get to it. There was not any light except for just a tiny bit at the very top of this tall room. This room represents that there was no way out of the holocaust.
There was almost no light at the end of the tunnel. No one could escape. The second axis was the Axis of the Exiles, and this led us to the Garden of Exiles. It is outside with massive concrete pieces with olive trees growing outside of it. They are all placed in a square. The interesting thing about this is that the garden is built on a tilted surface. So when you walk through, you are constantly off balance and running into the concrete pieces. It almost makes you sick to even walk through it. It is supposed to represent the tough lives of exiles and that there is never any stable ground. The 3rd axis was the Axis of Continuity, and this was the axis for the Jewish people still living. This museum was great!!!
(Received 5/11/05) From Andy Deig:
"After our plane arrived in Berlin and having traveled for almost an entire day, we got right out into the city and explored. We got used to the public transportation system.
"The second day, it rained and hailed, but it did not keep us in. We traveled to das Reichtages Gebäude, but on our way, we passed a very famous structure, the Brandenburger Tur.
"This was very amazing and one of the reasons why, is because there was a massive mural of the Brandenburger tur directly after the bombings of WWII and it shows the destruction of this monument and the city.
"The Reichtags building was also pretty cool. It showed a panoramic view of every possible angle of Berlin. T-hen we saw the Gedächtniskirche, which is now a historical exhibit of the history of Berlin.
"All of this has been awesome, but one of the best things that has happened to us so far is being able to interact with other Germans. Sometimes, we cannot understand a single word they say, but its great to try. The bars, the pubs, the supermarkets, and the restaurants are all great. The kultur here is amazing and unique. We cannot wait for the rest of this trip to come."
Deig is a Wabash College freshman from Evansville and member of the football team.
(Received 5/11/05) From Tim Rickard:
"My favorite part of today was getting separated from the group and having to find my way back. We were all at a Kaufhaus, and I stayed a little too long while the guys were finding their own way back.
"Discovering I could find my way around Berlin a little bit gives me a little bit more confidence with not only navigating, but also speaking the language. I know I knew enough German that I could survive. I also found that exercising is a great way to get to know the city. I got up at 6:00 and went running. I made a spiral around the Youthhostel and looked and tried to memorize all of the streets. I didn´t memorize them but I do know them when I see them.
"When we went to the Berliner Dom, I heard much of what I learned from the tutorial, which is kind of weird, but just goes to show you how versatile a liberal education is.
"The jury is still out on the application of all the 'victim of wars' instead of 'soldiers of wars' memorials. I guess it is just different than us. Anyway, The trip is going well, and I am hoping that the weather clears up."
Rickard is a Wabash College freshman from Valparaiso and runs track.
Professor Byrnes is accompanied on the trip by German Instructor Brian Tucker. Students on the trip are Samuel Clark, Andy Deig, Kyle O'Keefe, Matthew Potasnik, Tim Rickard, Kyle Weaver.


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Via Ars Technica: "After almost 10 years of testing, Quicksilver lays the beta tag to rest (if the lack of an eszett brings a tear to your eye, you can always reminisce by hitting option+S on your keyboard). What does this release mean? It means more than just a change in the version numbering system – it signifies a maturity of Quicksilver and a sign of what's to come." Quicksilver is fantastic for those of us for whom command+space and the dock combined is not enough. While I personally don't use Quicksilver (I use Spotlight through command+space to launch applications), I know of quite a few Mac users who swear by it.
Quicksilver moves out of beta
About The Author
Thom Holwerda
Follow me on Twitter @thomholwerda
4 Comments
- 2013-03-25 10:24 pmbowkota
Alfred is actually a much better app, particularly the workflows in the new version.
It's also better maintained; updated more frequently
- 2013-03-27 8:53 pmLoveQuicksilver
14 updates in 2 years for Quicksilver. I wouldn't call that slacking off.
After having used OS X 10.3 with Quicksilver, the whole UI just seemed so cumbersome and clumsy without it. Of course 10.3 was abandoned the second 10.4 was released, as is customary on the Mac, leaving me with a really nifty piece of extremely buggy abandonware, which in essence is what the second-to-latest version of OS X was as well. Keeping up with just the minimum specs for running updated software, even when it's free, soon becomes hideously expensive.
I suppose Quicksilver is not mandatory when you've got Spotlight, but nine years ago it really exposed how inefficient OS X actually was. I never really missed it on other platforms.
It's one of the first things I install on a new system. Once you're used to it it's hard to live without.


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eBay CEO John Donahoe's total compensation for 2013 plunged 53 percent compared with 2012, when the e-commerce company gave its leader more than $23 million in stock awards.
The San Jose company, which is sparring with activist investor Carl Icahn over board seats and its future direction, also gave Donahoe, 53, and other executives a lower-than-targeted incentive payment earlier this year. It noted that eBay missed financial targets last year, according to a regulatory filing.
eBay's compensation committee "concluded that while Mr. Donahoe continued to perform well against many … goals, the (company's) financial performance for 2013 and positioning relative to its competitors at the start of 2014 did not fully meet expectations," it said in a proxy statement filed Monday.
eBay's earnings rose 9 percent and revenue climbed 14 percent last year, as the company closed out 2013 with a strong holiday season. Its stock also rose 8 percent to end 2013 at $54.86, but the Standard & Poor's 500 index soared more than 29 percent last year.
eBay is fighting a push by Icahn to split the company in two by spinning off its PayPal payment arm, which is growing more quickly than the rest of the company. The investor also has nominated a pair of directors for eBay's board and complained about Donahoe's performance.
Icahn contends eBay sold video-chat site Skype prematurely, a move that cost shareholders $4 billion.
eBay is urging shareholders to support its directors, and it strongly believes PayPal should stay part of the company. No date has been set yet for the annual meeting at which shareholders will vote on the board and other issues.
For 2013, Donahoe received compensation totaling $13.8 million. That included a salary of $993,269 and a performance-based bonus of $1.6 million, which was down 43 percent. The company awarded this bonus earlier this year based on its 2013 performance.
Donahoe also received stock and option awards totaling about $11.1 million in 2013. That's less than half of what he received in 2012, when eBay gave the executive a one-time, $14.9 million grant as part of the company's performance-based incentive plan. Donahoe also received more than $20 million in stock and option awards in 2008, the year he became CEO.
Excluding the stock and option awards, Donahoe's compensation for 2013 fell 30 percent compared with 2012.
The Associated Press formula calculates an executive's total compensation during the last fiscal year by adding salary, bonuses, perks, and the estimated value of stock and stock options awarded during the year. The AP formula does not count changes in the present value of pension benefits.
eBay shares climbed 33 cents to $58.55 in midday trading Tuesday.


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DAYTONA BEACH, Fla. -- Every day for the past year, John Lajba would climb into his car, spark his engine and roll off to his studio in Omaha, Neb. During the familiar ride, his mind would wander, falling on the same topic and familiar image – Bill France Jr.
His thoughts about "The Man Who Made NASCAR" consumed him, and in turn, inspired him.
That's the life of a sculptor. Your life quickly morphs into that of your work. It's hard not to be envious of Lajba, especially when you learn the subject matter of his latest sculpture – former NASCAR Chairman and CEO Bill France Jr., which was unveiled Wednesday afternoon at Daytona International Speedway.
"I've been working on this sculpture for over a year, and every day I'd come to my studio and think about Bill France and how much class and charm he had," said Lajba, who also sculpts the Harley J. Earl Trophy given to each year's Daytona 500 winner. "I also knew that he was the type of man who could bring (people) together and have them do their best. And I want to do my best for him too, and that's what I did."
Daytona Beach, Fla.'s Mayor Glenn Ritchey came up with the idea, and Lajba was the easy choice to execute it. Lajba had already sculpted three statues that reside outside the World Center of Racing – likenesses of Bill France Sr., Annie B. France and Dale Earnhardt.
So now, outside the speedway's tour and ticket office, France's larger-than-life figure stands watching as masses enter his second home. The statue shows Bill Jr. wearing a sports coat over a shirt with the top button undone – relaxed, and ready to impart wisdom.
France's frozen-in-time look struck Daytona International Speedway President Joie Chitwood III more than any other feature.
"It's phenomenal," Chitwood said. "It's Bill Jr. It's him. I can almost hear the words coming out of his mouth when I see his expression. I'm so proud we have this statue on our property and that fans are going to get to see it every day."
Bill Jr.'s brother Jim, the chairman of International Speedway Corp. (ISC), was on hand for the unveiling. So too were his children Brian, NASCAR's current chairman and CEO, and Lesa, the chief executive officer of ISC. And, of course, his wife Betty Jane was there, offering a unique perspective held only to those who truly knew Bill Jr.
"Bill has a painting in his dressing room which always makes me smile when I walk by it," Betty Jane France said. "It's a young boy looking at his image in the mirror. But instead of seeing himself as a young boy that he is, he sees a mature young man – all dressed exactly like he is – staring back at him. I think that sums up the way Bill France was always looking forward into the future. The caption under the picture reads 'Clothes make the man' and on this day, John Lajba [showed] how he not only made the clothes, but he made the great image of a wonderful man who stepped out of a painting and made the world a better place."
The bronze likeness of Bill Jr. depicts him with his arms gently hanging at their side – again, relaxed. He wears glasses in the sculpture, with his hair neatly parted to the right. His lips are parted, as if ready to speak. Those who knew Bill Jr. could only imagine what words would eventually tumble out.
After numerous visits with Mrs. France, and digging through hundreds of photos, this was the image on which Lajba settled. Chitwood said it was the exact right direction, even though he could have gone any number of ways.
"We laughed about it," Chitwood said. "Everyone has their image of Bill and how he used to act. Of course, he was a favorite of hot dogs, we all thought about the things we would love to see Bill doing. But I think his expression is so perfect. It looks like he's thinking about something, and he's ready to tell you what he's thinking."
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After writing about the nominations for Top 40 in a previous blog post To Scramble or Not to Scramble..., you can probably imagine my surprise at actually being voted into a top place! Especially because I didn't scramble.
So thank you to all my loyal readers who voted for my blog! I appreciate your support so much, it makes the hard work of writing a daily blog worthwhile. Knowing that someone (or several someones) out there enjoy or gain knowledge from what I write means the world to me.
I'm one of 5 in the "Everything" Category. I love the description of an "Everything" blog:
"These are the "variety shows" of geneablogs. Find a little (or a lot) of everything here: news releases, product reviews, tips, finds, rants, raves, shout-outs and humor."I also chuckled over their statement that I've been blogging since "the Middle Ages"! It's true, I started blogging in 2003 before most people had even heard of a blog. Since Feb 9, 2003 to March 9, 2011 I've written 1,141 blog posts (I didn't write very often the first 4 years my blog was online). My family always says I like to talk! And I guess that's the proof.
Being voted in to Top 40 with 39 other amazing blogs has my head spinning and I'm honoured to be in such good company. I hope if you haven't read the rest of those on the list that you'll take a few minutes to have a look at them.


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DS-2 Chronograph 1/100 sec
Reference: C024.447.22.051.00
Diameter: 41.00 mm
The sports design, coloured elements and elegant contours of these innovative chronographs make them ideal adornments to the wrist, enhanced by the fascinating 1/100th second display.
CHF 940.- Recommended retail price (incl. VAT)
Case: DS Concept Stainless steel 316L
Bezel: Stainless steel 316L PVD coating Aluminium Tachymeter
Glass: Sapphire crystal Antireflection treatment on both sides
Dial: Black Index
Movement: Swiss Made Quartz PrecidriveTM End Of Life Time-zone mechanism 1/100 sec in real time Chronograph 12 h
Water resistance: Up to a pressure of 10 bar (100m)
Bracelet: Stainless steel 316L PVD coating
Buckle: Stainless steel 316L Butterfly clasp with 2 push-buttons
Raised case back
The symbol of the turtle, used by Certina since the 1960s, stands for robustness and longevity. Today it can be found not just in the brand logo, but on every single Certina watch. The turtle creates a lasting impression in elaborate 3-D on the case back.
A 1/100th second in real time
The display showing 1/100th seconds in real time not only allows for highly precise time measurements, it is also fascinating to watch. In order to preserve a watch's energy reserves, the coloured 1/100th second hand stops after one minute, whilst the central stop second measures time unwaveringly, to exactly 1/100th of a second.


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The Three Wise Men have come calling at all Wine World locations, and it's not even Christmas yet.
The Three Wise Men is the label for the store's own wine, which recently made its debut.
The label bears the likeness of the stores' partner owners, Chan Cox and Robert Montgomery, and sales director and vice-president Michel Thibault.
Thibault said the store is always seeking new wine from producers specializing in high quality.
In their search, the three men visit properties all over the world, including South Africa, Argentina, and France, tasting a hundred or even a thousand wines.
While in France last year, the trio sampled around 800 wines.
"It was painstaking but necessary to find the ones we think are best and that our customers will like," said Thibault.
When the men tasted one particular wine, it struck the right note and it now bears their label.
"This one is a rising star in wines and made by Jamie Kutch, who is also a rising star as a winemaker and specializes in high-end pinot noirs and chardonnays. His style is both elegance and balance and his wines are now getting huge ratings by the likes of Robert Parker and others," said Thibault.
Originally a New York broker, Kutch moved to California where he apprenticed with some of the top wine makers in America, learning how to be a top producer of pinot noir and chardonnay, before establishing his Kutch Wines label.
Thibault describes the chardonnay as oaky and buttery as it is aged in wood barrels, with fruit forward.
"A lot of elegance. The mark of this maker," he said.
However, Thibault said the pinot noir is probably what Kutch does best.
"It's not overly light and the tannins are integrated with a great balance and a soft long finish � one of the pleasures of wine tasting � leaving a feeling of elegance and pleasure," he said.
Thibault said the cabernet is rich and full bodied with black cherry notes.
And, he said all three are an incredible value at $14 to $17.
"In buying wines for our stores, ones we want are often out of stock and prices keep rising. With bottling our own, the quality and value we're able to offer is beyond ordinary. By going directly to producer we have a quality product that is really good and it is getting a lot of attention," he said. "Kutch is a young man who is getting huge ratings and we knew we had something special in his wines. Now, all we needed was a label. We wanted a label that had meaning to us."
A year or so ago the men traveled to Bordeaux to the largest wine market in the world, VINEXPO. The show features thousands of producers from all over the world. When the show was over, they were invited to the Chateau Lascombes estate, along with 1,500 other guests, for the event of the year: the Festival of Flowers.
This event is hosted each year by a different chateau and property owners spare no expense for the party. Film stars, political personalities, and the who's who of the wine business attended the most memorable party.
Guests were served appetizers of fresh oysters and foie gras in late afternoon while helicopters ferried in guests, all in tux and gowns. Music from a quartet of American singers followed and an induction of new members to the wine brotherhood. Inductees paraded about, wearing beautiful gold, purple and green long robes indicating their status and membership. The castle is ivied and offered a magnificent backdrop to all this activity.
As evening fell, lights came on all over the walls of the building, giving off an eerie, amazing feel, said Thibault. Guests were moved to a number of large, beautifully decorated tents where dinner was served. Dinner began with fresh local caviar and truffled mashed potatoes served by hundreds of waiters dressed in black and wine waiters dressed in white. The foods were prepared by the top chefs in France.
However, Thibault said the food paled in comparison to the wines that were served: Chateau Haut Brion Blanc, Chateau Lascombes 2005, Chateau Leoville Poyferre 2000 or Chateau d'Yquem 2008, costing $300 to $400.
For dessert, they were treated to a selection that is usually reserved for small groups of four to six people.
"I believe that each person's meal must have cost about $1,300 to $1,500 and that does not account for tent rental, police and security forces. You can see how memorable this event was for us and since we arrived early, we had the best seats.�
They took a photo to remember the occasion and that�s what became the label for Three Wise Men.
�The label is and always will be a great reminder of this function we were lucky and wise to attend," he said.


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Data Mining Techniques
- Data Mining
- Crucial Concepts in Data Mining
- Data Warehousing
- On-Line Analytic Processing (OLAP)
- Exploratory Data Analysis (EDA) and Data Mining Techniques
- Neural Networks
Data Mining
Data Mining is an analytic process designed to explore data (usually large amounts of data – typically business or market related – also known as "big data") in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining and one that has the most direct business applications. The process of data mining consists of three stages: (1) the initial exploration, (2) model building or pattern identification with validation/verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions).
Stage 1: Exploration. This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and – in case of data sets with large numbers of variables ("fields") – performing some preliminary feature selection operations to bring the number of variables to a manageable range (depending on the statistical methods which are being considered). Then, depending on the nature of the analytic problem, this first stage of the process of data mining may involve anywhere between a simple choice of straightforward predictors for a regression model, to elaborate exploratory analyses using a wide variety of graphical and statistical methods (see Exploratory Data Analysis (EDA)) in order to identify the most relevant variables and determine the complexity and/or the general nature of models that can be taken into account in the next stage.
Stage 2: Model building and validation. This stage involves considering various models and choosing the best one based on their predictive performance (i.e., explaining the variability in question and producing stable results across samples). This may sound like a simple operation, but in fact, it sometimes involves a very elaborate process. There are a variety of techniques developed to achieve that goal – many of which are based on so-called "competitive evaluation of models," that is, applying different models to the same data set and then comparing their performance to choose the best. These techniques – which are often considered the core of predictive data mining – include: Bagging (Voting, Averaging), Boosting, Stacking (Stacked Generalizations), and Meta-Learning.
Stage 3: Deployment. That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.
The concept of Data Mining is becoming increasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty. Recently, there has been increased interest in developing new analytic techniques specifically designed to address the issues relevant to business Data Mining (e.g., Classification Trees), but Data Mining is still based on the conceptual principles of statistics including the traditional Exploratory Data Analysis (EDA) and modeling and it shares with them both some components of its general approaches and specific techniques.
However, an important general difference in the focus and purpose between Data Mining and the traditional Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the basic nature of the underlying phenomena. In other words, Data Mining is relatively less concerned with identifying the specific relations between the involved variables. For example, uncovering the nature of the underlying functions or the specific types of interactive, multivariate dependencies between variables are not the main goal of Data Mining. Instead, the focus is on producing a solution that can generate useful predictions. Therefore, Data Mining accepts among others a "black box" approach to data exploration or knowledge discovery and uses not only the traditional Exploratory Data Analysis (EDA) techniques, but also such techniques as Neural Networks which can generate valid predictions but are not capable of identifying the specific nature of the interrelations between the variables on which the predictions are based.
Data Mining is often considered to be "a blend of statistics, AI (artificial intelligence), and data base research" (Pregibon, 1997, p. 8), which until very recently was not commonly recognized as a field of interest for statisticians, and was even considered by some "a dirty word in Statistics" (Pregibon, 1997, p. 8). Due to its applied importance, however, the field emerges as a rapidly growing and major area (also in statistics) where important theoretical advances are being made (see, for example, the recent annual International Conferences on Knowledge Discovery and Data Mining, co-hosted by the American Statistical Association).
For information on Data Mining techniques, review the summary topics included below. There are numerous books that review the theory and practice of data mining; the following books offer a representative sample of recent general books on data mining, representing a variety of approaches and perspectives:
Berry, M., J., A., & Linoff, G., S., (2000). Mastering data mining. New York: Wiley.
Edelstein, H., A. (1999). Introduction to data mining and knowledge discovery (3rd ed). Potomac, MD: Two Crows Corp.
Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery & data mining. Cambridge, MA: MIT Press.
Han, J., Kamber, M. (2000). Data mining: Concepts and Techniques. New York: Morgan-Kaufman.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer.
Pregibon, D. (1997). Data Mining. Statistical Computing and Graphics, 7, 8.
Weiss, S. M., & Indurkhya, N. (1997). Predictive data mining: A practical guide. New York: Morgan-Kaufman.
Westphal, C., Blaxton, T. (1998). Data mining solutions. New York: Wiley.
Witten, I. H., & Frank, E. (2000). Data mining. New York: Morgan-Kaufmann.
Crucial Concepts in Data Mining
Bagging (Voting, Averaging)
The concept of bagging (voting for classification, averaging for regression-type problems with continuous dependent variables of interest) applies to the area of predictive data mining, to combine the predicted classifications (prediction) from multiple models, or from the same type of model for different learning data. It is also used to address the inherent instability of results when applying complex models to relatively small data sets. Suppose your data mining task is to build a model for predictive classification, and the dataset from which to train the model (learning data set, which contains observed classifications) is relatively small. You could repeatedly sub-sample (with replacement) from the dataset, and apply, for example, a tree classifier (e.g., C&RT and CHAID) to the successive samples. In practice, very different trees will often be grown for the different samples, illustrating the instability of models often evident with small data sets. One method of deriving a single prediction (for new observations) is to use all trees found in the different samples, and to apply some simple voting: The final classification is the one most often predicted by the different trees. Note that some weighted combination of predictions (weighted vote, weighted average) is also possible, and commonly used. A sophisticated (machine learning) algorithm for generating weights for weighted prediction or voting is the Boosting procedure.
The concept of boosting applies to the area of predictive data mining, to generate multiple models or classifiers (for prediction or classification), and to derive weights to combine the predictions from those models into a single prediction or predicted classification (see also Bagging).
A simple algorithm for boosting works like this: Start by applying some method (e.g., a tree classifier such as C&RT or CHAID) to the learning data, where each observation is assigned an equal weight. Compute the predicted classifications, and apply weights to the observations in the learning sample that are inversely proportional to the accuracy of the classification. In other words, assign greater weight to those observations that were difficult to classify (where the misclassification rate was high), and lower weights to those that were easy to classify (where the misclassification rate was low). In the context of C&RT for example, different misclassification costs (for the different classes) can be applied, inversely proportional to the accuracy of prediction in each class. Then apply the classifier again to the weighted data (or with different misclassification costs), and continue with the next iteration (application of the analysis method for classification to the re-weighted data).
Boosting will generate a sequence of classifiers, where each consecutive classifier in the sequence is an "expert" in classifying observations that were not well classified by those preceding it. During deployment (for prediction or classification of new cases), the predictions from the different classifiers can then be combined (e.g., via voting, or some weighted voting procedure) to derive a single best prediction or classification.
Note that boosting can also be applied to learning methods that do not explicitly support weights or misclassification costs. In that case, random sub-sampling can be applied to the learning data in the successive steps of the iterative boosting procedure, where the probability for selection of an observation into the subsample is inversely proportional to the accuracy of the prediction for that observation in the previous iteration (in the sequence of iterations of the boosting procedure).
See Models for Data Mining.
Data Preparation (in Data Mining)
Data preparation and cleaning is an often neglected but extremely important step in the data mining process. The old saying "garbage-in-garbage-out" is particularly applicable to the typical data mining projects where large data sets collected via some automatic methods (e.g., via the Web) serve as the input into the analyses. Often, the method by which the data where gathered was not tightly controlled, and so the data may contain out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), and the like. Analyzing data that has not been carefully screened for such problems can produce highly misleading results, in particular in predictive data mining.
Data Reduction (for Data Mining)
The term Data Reduction in the context of data mining is usually applied to projects where the goal is to aggregate or amalgamate the information contained in large datasets into manageable (smaller) information nuggets. Data reduction methods can include simple tabulation, aggregation (computing descriptive statistics) or more sophisticated techniques like clustering, principal components analysis, etc.
See also predictive data mining, drill-down analysis.
The concept of deployment in predictive data mining refers to the application of a model for prediction or classification to new data. After a satisfactory model or set of models has been identified (trained) for a particular application, we usually want to deploy those models so that predictions or predicted classifications can quickly be obtained for new data. For example, a credit card company may want to deploy a trained model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions which have a high probability of being fraudulent.
Drill-Down Analysis
The concept of drill-down analysis applies to the area of data mining, to denote the interactive exploration of data, in particular of large databases. The process of drill-down analyses begins by considering some simple break-downs of the data by a few variables of interest (e.g., Gender, geographic region, etc.). Various statistics, tables, histograms, and other graphical summaries can be computed for each group. Next, we may want to "drill-down" to expose and further analyze the data "underneath" one of the categorizations, for example, we might want to further review the data for males from the mid-west. Again, various statistical and graphical summaries can be computed for those cases only, which might suggest further break-downs by other variables (e.g., income, age, etc.). At the lowest ("bottom") level are the raw data: For example, you may want to review the addresses of male customers from one region, for a certain income group, etc., and to offer to those customers some particular services of particular utility to that group.
Feature Selection
One of the preliminary stage in predictive data mining, when the data set includes more variables than could be included (or would be efficient to include) in the actual model building phase (or even in initial exploratory operations), is to select predictors from a large list of candidates. For example, when data are collected via automated (computerized) methods, it is not uncommon that measurements are recorded for thousands or hundreds of thousands (or more) of predictors. The standard analytic methods for predictive data mining, such as neural network analyses, classification and regression trees, generalized linear models, or general linear models become impractical when the number of predictors exceed more than a few hundred variables.
Feature selection selects a subset of predictors from a large list of candidate predictors without assuming that the relationships between the predictors and the dependent or outcome variables of interest are linear, or even monotone. Therefore, this is used as a pre-processor for predictive data mining, to select manageable sets of predictors that are likely related to the dependent (outcome) variables of interest, for further analyses with any of the other methods for regression and classification.
Machine Learning
Machine learning, computational learning theory, and similar terms are often used in the context of Data Mining, to denote the application of generic model-fitting or classification algorithms for predictive data mining. Unlike traditional statistical data analysis, which is usually concerned with the estimation of population parameters by statistical inference, the emphasis in data mining (and machine learning) is usually on the accuracy of prediction (predicted classification), regardless of whether or not the "models" or techniques that are used to generate the prediction is interpretable or open to simple explanation. Good examples of this type of technique often applied to predictive data mining are neural networks or meta-learning techniques such as boosting, etc. These methods usually involve the fitting of very complex "generic" models, that are not related to any reasoning or theoretical understanding of underlying causal processes; instead, these techniques can be shown to generate accurate predictions or classification in crossvalidation samples.
The concept of meta-learning applies to the area of predictive data mining, to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different. In this context, this procedure is also referred to as Stacking (Stacked Generalization).
Suppose your data mining project includes tree classifiers, such as C&RT and CHAID, linear discriminant analysis (e.g., see GDA), and Neural Networks. Each computes predicted classifications for a crossvalidation sample, from which overall goodness-of-fit statistics (e.g., misclassification rates) can be computed. Experience has shown that combining the predictions from multiple methods often yields more accurate predictions than can be derived from any one method (e.g., see Witten and Frank, 2000). The predictions from different classifiers can be used as input into a meta-learner, which will attempt to combine the predictions to create a final best predicted classification. So, for example, the predicted classifications from the tree classifiers, linear model, and the neural network classifier(s) can be used as input variables into a neural network meta-classifier, which will attempt to "learn" from the data how to combine the predictions from the different models to yield maximum classification accuracy.
We can apply meta-learners to the results from different meta-learners to create "meta-meta"-learners, and so on; however, in practice such exponential increase in the amount of data processing, in order to derive an accurate prediction, will yield less and less marginal utility.
Models for Data Mining
In the business environment, complex data mining projects may require the coordinate efforts of various experts, stakeholders, or departments throughout an entire organization. In the data mining literature, various "general frameworks" have been proposed to serve as blueprints for how to organize the process of gathering data, analyzing data, disseminating results, implementing results, and monitoring improvements.
One such model, CRISP (Cross-Industry Standard Process for data mining) was proposed in the mid-1990s by a European consortium of companies to serve as a non-proprietary standard process model for data mining. This general approach postulates the following (perhaps not particularly controversial) general sequence of steps for data mining projects:
Another approach – the Six Sigma methodology – is a well-structured, data-driven methodology for eliminating defects, waste, or quality control problems of all kinds in manufacturing, service delivery, management, and other business activities. This model has recently become very popular (due to its successful implementations) in various American industries, and it appears to gain favor worldwide. It postulated a sequence of, so-called, DMAIC steps –
– that grew up from the manufacturing, quality improvement, and process control traditions and is particularly well suited to production environments (including "production of services," i.e., service industries).
Another framework of this kind (actually somewhat similar to Six Sigma) is the approach proposed by SAS Institute called SEMMA –
– which is focusing more on the technical activities typically involved in a data mining project.
All of these models are concerned with the process of how to integrate data mining methodology into an organization, how to "convert data into information," how to involve important stake-holders, and how to disseminate the information in a form that can easily be converted by stake-holders into resources for strategic decision making.
Some software tools for data mining are specifically designed and documented to fit into one of these specific frameworks.
The general underlying philosophy of StatSoft's STATISTICA Data Miner is to provide a flexible data mining workbench that can be integrated into any organization, industry, or organizational culture, regardless of the general data mining process-model that the organization chooses to adopt. For example, STATISTICA Data Miner can include the complete set of (specific) necessary tools for ongoing company wide Six Sigma quality control efforts, and users can take advantage of its (still optional) DMAIC-centric user interface for industrial data mining tools. It can equally well be integrated into ongoing marketing research, CRM (Customer Relationship Management) projects, etc. that follow either the CRISP or SEMMA approach – it fits both of them perfectly well without favoring either one. Also, STATISTICA Data Miner offers all the advantages of a general data mining oriented "development kit" that includes easy to use tools for incorporating into your projects not only such components as custom database gateway solutions, prompted interactive queries, or proprietary algorithms, but also systems of access privileges, workgroup management, and other collaborative work tools that allow you to design large scale, enterprise-wide systems (e.g., following the CRISP, SEMMA, or a combination of both models) that involve your entire organization.
Predictive Data Mining
The term Predictive Data Mining is usually applied to identify data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models (e.g., neural networks, meta-learner) that can quickly identify transactions which have a high probability of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g., to identify cluster or segments of customers), in which case drill-down descriptive and exploratory methods would be applied. Data reduction is another possible objective for data mining (e.g., to aggregate or amalgamate the information in very large data sets into useful and manageable chunks).
See Models for Data Mining.
Stacked Generalization
See Stacking.
Stacking (Stacked Generalization)
The concept of stacking (Stacked Generalization) applies to the area of predictive data mining, to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different.
Suppose your data mining project includes tree classifiers, such as C&RT or CHAID, linear discriminant analysis (e.g., see GDA), and Neural Networks. Each computes predicted classifications for a crossvalidation sample, from which overall goodness-of-fit statistics (e.g., misclassification rates) can be computed. Experience has shown that combining the predictions from multiple methods often yields more accurate predictions than can be derived from any one method (e.g., see Witten and Frank, 2000). In stacking, the predictions from different classifiers are used as input into a meta-learner, which attempts to combine the predictions to create a final best predicted classification. So, for example, the predicted classifications from the tree classifiers, linear model, and the neural network classifier(s) can be used as input variables into a neural network meta-classifier, which will attempt to "learn" from the data how to combine the predictions from the different models to yield maximum classification accuracy.
Other methods for combining the prediction from multiple models or methods (e.g., from multiple datasets used for learning) are Boosting and Bagging (Voting).
Text Mining
While Data Mining is typically concerned with the detection of patterns in numeric data, very often important (e.g., critical to business) information is stored in the form of text. Unlike numeric data, text is often amorphous, and difficult to deal with. Text mining generally consists of the analysis of (multiple) text documents by extracting key phrases, concepts, etc. and the preparation of the text processed in that manner for further analyses with numeric data mining techniques (e.g., to determine co-occurrences of concepts, key phrases, names, addresses, product names, etc.).
See Bagging.
Data Warehousing
StatSoft defines data warehousing as a process of organizing the storage of large, multivariate data sets in a way that facilitates the retrieval of information for analytic purposes.
The most efficient data warehousing architecture will be capable of incorporating or at least referencing all data available in the relevant enterprise-wide information management systems, using designated technology suitable for corporate data base management (e.g., Oracle, Sybase, MS SQL Server. Also, a flexible, high-performance (see the IDP technology), open architecture approach to data warehousing – that flexibly integrates with the existing corporate systems and allows the users to organize and efficiently reference for analytic purposes enterprise repositories of data of practically any complexity – is offered in StatSoft enterprise systems such as STATISTICA Enterprise and STATISTICA Enterprise/QC , which can also work in conjunction with STATISTICA Data Miner and STATISTICA Enterprise Server.
On-Line Analytic Processing (OLAP)
The term On-Line Analytic Processing – OLAP (or Fast Analysis of Shared Multidimensional Information – FASMI) refers to technology that allows users of multidimensional databases to generate on-line descriptive or comparative summaries ("views") of data and other analytic queries. Note that despite its name, analyses referred to as OLAP do not need to be performed truly "on-line" (or in real-time); the term applies to analyses of multidimensional databases (that may, obviously, contain dynamically updated information) through efficient "multidimensional" queries that reference various types of data. OLAP facilities can be integrated into corporate (enterprise-wide) database systems and they allow analysts and managers to monitor the performance of the business (e.g., such as various aspects of the manufacturing process or numbers and types of completed transactions at different locations) or the market. The final result of OLAP techniques can be very simple (e.g., frequency tables, descriptive statistics, simple cross-tabulations) or more complex (e.g., they may involve seasonal adjustments, removal of outliers, and other forms of cleaning the data). Although Data Mining techniques can operate on any kind of unprocessed or even unstructured information, they can also be applied to the data views and summaries generated by OLAP to provide more in-depth and often more multidimensional knowledge. In this sense, Data Mining techniques could be considered to represent either a different analytic approach (serving different purposes than OLAP) or as an analytic extension of OLAP.
Exploratory Data Analysis (EDA)
EDA vs. Hypothesis Testing
As opposed to traditional hypothesis testing designed to verify a priori hypotheses about relations between variables (e.g., "There is a positive correlation between the AGE of a person and his/her RISK TAKING disposition"), exploratory data analysis (EDA) is used to identify systematic relations between variables when there are no (or not complete) a priori expectations as to the nature of those relations. In a typical exploratory data analysis process, many variables are taken into account and compared, using a variety of techniques in the search for systematic patterns.
Computational EDA techniques
Computational exploratory data analysis methods include both simple basic statistics and more advanced, designated multivariate exploratory techniques designed to identify patterns in multivariate data sets.
Basic statistical exploratory methods. The basic statistical exploratory methods include such techniques as examining distributions of variables (e.g., to identify highly skewed or non-normal, such as bi-modal patterns), reviewing large correlation matrices for coefficients that meet certain thresholds (see example above), or examining multi-way frequency tables (e.g., "slice by slice" systematically reviewing combinations of levels of control variables).
Multivariate exploratory techniques. Multivariate exploratory techniques designed specifically to identify patterns in multivariate (or univariate, such as sequences of measurements) data sets include: Cluster Analysis, Factor Analysis, Discriminant Function Analysis, Multidimensional Scaling, Log-linear Analysis, Canonical Correlation, Stepwise Linear and Nonlinear (e.g., Logit) Regression, Correspondence Analysis, Time Series Analysis, and Classification Trees.
Neural Networks. Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data.
For more information, see Neural Networks; see also STATISTICA Neural Networks.
Graphical (Data Visualization) EDA Techniques
A large selection of powerful exploratory data analytic techniques is also offered by graphical data visualization methods that can identify relations, trends, and biases "hidden" in unstructured data sets.
Brushing. Perhaps the most common and historically first widely used technique explicitly identified as graphical exploratory data analysis is brushing, an interactive method allowing us to select on-screen specific data points or subsets of data and identify their (e.g., common) characteristics, or to examine their effects on relations between relevant variables. Those relations between variables can be visualized by fitted functions (e.g., 2D lines or 3D surfaces) and their confidence intervals, thus, for example, we can examine changes in those functions by interactively (temporarily) removing or adding specific subsets of data. For example, one of many applications of the brushing technique is to select (i.e., highlight) in a matrix scatterplot all data points that belong to a certain category (e.g., a "medium" income level, see the highlighted subset in the fourth component graph of the first row in the illustration left) in order to examine how those specific observations contribute to relations between other variables in the same data set (e.g, the correlation between the "debt" and "assets" in the current example). If the brushing facility supports features such as "animated brushing" or "automatic function re-fitting," we can define a dynamic brush that would move over the consecutive ranges of a criterion variable (e.g., "income" measured on a continuous scale or a discrete [3-level] scale as on the illustration above) and examine the dynamics of the contribution of the criterion variable to the relations between other relevant variables in the same data set.
Other graphical EDA techniques. Other graphical exploratory analytic techniques include function fitting and plotting, data smoothing, overlaying and merging of multiple displays, categorizing data, splitting/merging subsets of data in graphs, aggregating data in graphs, identifying and marking subsets of data that meet specific conditions, icon plots,
shading, plotting confidence intervals and confidence areas (e.g., ellipses),
generating tessellations, spectral planes,
integrated layered compressions,
and projected contours, data image reduction techniques, interactive (and continuous) rotation
with animated stratification (cross-sections) of 3D displays, and selective highlighting of specific series and blocks of data.
Verification of Results of EDA
The exploration of data can only serve as the first stage of data analysis and its results can be treated as tentative at best as long as they are not confirmed, e.g., crossvalidated, using a different data set (or an independent subset). If the result of the exploratory stage suggests a particular model, then its validity can be verified by applying it to a new data set and testing its fit (e.g., testing its predictive validity). Case selection conditions can be used to quickly define subsets of data (e.g., for estimation and verification), and for testing the robustness of results.
Neural Networks
(see also, the Neural Networks topic)
Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. Neural Networks is one of the Data Mining techniques.
The first step is to design a specific network architecture (that includes a specific number of "layers" each consisting of a certain number of "neurons"). The size and structure of the network needs to match the nature (e.g., the formal complexity) of the investigated phenomenon. Because the latter is obviously not known very well at this early stage, this task is not easy and often involves multiple "trials and errors." (Now, there is, however, neural network software that applies artificial intelligence techniques to aid in that tedious task and finds "the best" network architecture.)
The new network is then subjected to the process of "training." In that phase, neurons apply an iterative process to the number of inputs (variables) to adjust the weights of the network in order to optimally predict (in traditional terms, we could say find a "fit" to) the sample data on which the "training" is performed. After the phase of learning from an existing data set, the new network is ready and it can then be used to generate predictions.
The resulting "network" developed in the process of "learning" represents a pattern detected in the data. Thus, in this approach, the "network" is the functional equivalent of a model of relations between variables in the traditional model building approach. However, unlike in the traditional models, in the "network," those relations cannot be articulated in the usual terms used in statistics or methodology to describe relations between variables (such as, for example, "A is positively correlated with B but only for observations where the value of C is low and D is high"). Some neural networks can produce highly accurate predictions; they represent, however, a typical a-theoretical (one can say, "a black box") research approach. That approach is concerned only with practical considerations, that is, with the predictive validity of the solution and its applied relevance and not with the nature of the underlying mechanism or its relevance for any "theory" of the underlying phenomena.
However, it should be mentioned that Neural Network techniques can also be used as a component of analyses designed to build explanatory models because Neural Networks can help explore data sets in search for relevant variables or groups of variables; the results of such explorations can then facilitate the process of model building. Moreover, now there is neural network software that uses sophisticated algorithms to search for the most relevant input variables, thus potentially contributing directly to the model building process.
One of the major advantages of neural networks is that, theoretically, they are capable of approximating any continuous function, and thus the researcher does not need to have any hypotheses about the underlying model, or even to some extent, which variables matter. An important disadvantage, however, is that the final solution depends on the initial conditions of the network, and, as stated before, it is virtually impossible to "interpret" the solution in traditional, analytic terms, such as those used to build theories that explain phenomena.
Some authors stress the fact that neural networks use, or we should say are expected to use, massively parallel computation models. For example Haykin (1994) defines neural network as:
"a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects: (1) Knowledge is acquired by the network through a learning process, and (2) Interneuron connection strengths known as synaptic weights are used to store the knowledge." (p. 2).
However, as Ripley (1996) points out, the vast majority of contemporary neural network applications run on single-processor computers and he argues that a large speed-up can be achieved not only by developing software that will take advantage of multiprocessor hardware by also by designing better (more efficient) learning algorithms.
Neural networks is one of the methods used in Data Mining; see also Exploratory Data Analysis. For more information on neural networks, see Haykin (1994), Masters (1995), Ripley (1996), and Welstead (1994). For a discussion of neural networks as statistical tools, see Warner and Misra (1996). See also, STATISTICA Neural Networks.
Posted on June 22, 2012, in Uncategorized and tagged data mining, techniques. Bookmark the permalink. Leave a comment.


text:
May 052013
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Here's a short guide for students on how to make the most of Digimap in coursework projects.
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text:
The Wildcats got off to a slow start but pulled away late in the first half to earn their second national title in three seasons.
SAN ANTONIO — You just never know which weapon it's going to be.
Donte DiVincenzo had the game of his life, scoring 13 of his season-high 31 points during the second half, and Villanova rolled to its second national championship in three seasons by topping Michigan, 79-62, at the Alamodome on Monday night.
Michigan got off to a strong start in the game, but didn't have the muscle to stick with the Wildcats.
Despite Associated Press Player of the Year Jalen Brunson missing a long stretch of the second half in foul trouble and going more than 18 minutes without making a shot in the first half, Villanova continued its dominant run through the tournament field.
DiVincenzo was hot early and stayed that way, going 10-for-15 from the field, including 5-for-7 from 3-point range.
But DiVincenzo was quick to credit his teammates for his performance."Honestly, it was a credit to these guys up here," DiVincenzo said, pointing to his teammates. "(Michigan) did a great job of making it difficult for Jalen (Brunson) and Mikel (Bridges), and they were just making the right play. They were trying to be aggressive, and they were finding me, and Omari (Spellman) was setting great screens and getting me open."
After hitting a Final Four-record 18 3-pointers on Saturday, the Wildcats were 10-for-27 from long distance against Michigan.
Mikal Bridges had 19 for Villanova. Brunson added 9. Eric Paschall was held scoreless in the first half and finished with 6.
Muhammad-Ali Abdur-Rahkman had 23 for Michigan. Moe Wagner added 16.
Bridges, Brunson and Paschall were named to the All-Tournament team for Villanova. Wagner made the squad for Michigan. DiVincenzo was named the tournament's Most Outstanding Player.
Michigan struggled from from the 3-point range throughout the game, going 3-for-23 in the game, including 1-10 in the second half.
The Wolverines came out firing and were up as many as six early in the first half, but despite the Wildcats playing poorly, couldn't pull away. As the Wildcats found their footing, they started to dominate.
"They came out, you know, John Beilein is one of the best coaches of all time, and early we weren't prepared for some of the things they were doing," Villanova coach Jay Wright said. "I should have known better with John. Then, we kind of got settled in after that and did a good job defensively, but we have great respect for them, and it was just an honor to play them in this game."
Led by DiVincenzo, Villanova went on a run, finishing the first half by scoring 19 of the final 32 points to take a 37-28 lead into the break.
The second half was more of the same, with the Wildcats slowly stretching their lead. With just under 10 minutes remaining, Michigan took its final swing.
A Wagner dunk and a Charles Matthews layup cut the Villanova lead to 12, but DiVincenzo — with Brunson on the bench with four fouls — put the game away. The sophomore hit a pair of 3-pointers, taking the lead back to 18 and ending Michigan's hopes.
"We couldn't get it going on either end, and let's credit Villanova in both respects," Michigan coach John Beilein said. "They play really good defense. Everybody talks about their offense, but I think that's what's really underrated is how good they are defensively."


text:
Saturday, February 21, 2009
15 comments:
- Leonard Chang said...
Thanks for the update. I love getting the info on the process and the inner workings...
How many writers are you working with, and how much input do they have on the storylines/arc/etc?
- Feb 22, 2009, 7:41:00 AM
- Parthenians said...
"Alliances"...make or break life; with your brothers, sisters, neighbors, sons, daughters, wife, husband, your dog, your cat, the planet, yourself, and this moment.
Alliances are the sugar-tit of reality: not because they are comforting, though they may well be, but because an "ally" is a reflection of you giving up yourself to another for the opportunity to be creative, and as you have said, Kurt, to play God.
I look forward to the "alliances" and what they will nurture within me. That is the beauty and truth of your writing, Kurt. You fucking do your job really well.
I spoke recently to a sheriff in Tombstone who stills packs a Colt six-shooter at his side. He commented that it was hard getting old, watching his allies die off, move away, just forget who they are. As for getting older, I said, anything that's not attentive and allied to this moment is
a waste of our remaining time. And laughter and good friends fill the heart in timeless ways...
Thanks for your heart, Kurt..
- Feb 22, 2009, 9:21:00 AM
- Jacob said...
Season two cannot come any sooner.
- Feb 22, 2009, 1:12:00 PM
- Day-Dream-Queen said...
I cannot wait to see the second season of SOA. I absolutely LOVED the first season. I am very curious to see what kind of alliances are going to be formed or are going to get broken in the second season. But no matter what happens in the second season I'm know I will enjoy watching the show because you are an amazing writer and none of your other works have ever disappointed me.
- Feb 22, 2009, 1:44:00 PM
- surrounded by carnivores said...
Thankyou for the update. And congratulations on the enthusiasm of the network. That is a feat in itself. Good to have someone 'up there' who admires good storytelling. We are turning increasingly to the 'lesser' channels (not the big 3)to be entertained and enriched.
I agree with Parthenians regarding alliances/allies. To have friends who understand your times, your thoughts, etc. is a gift that can enhance or destroy. They can be enriching/expanding or lead one further into delusion. As they leave (or die) those ties peel away and one is left with oneself ... nothing more.
Ah! a former Catholic too. 12 years in parochial schools (with nuns:my ears still ring from the dictionaries pounded upside my head. I will still go in for my smudge of ashes, tho, on Wednesday to be reminded. Pace
- Feb 22, 2009, 1:50:00 PM
- Brujah said...
Just wanted to say keep up with the good work. Love the show , love the acting in it, especially Ron hes a big inspiration, well I'll stop now seeing as you most likely get tired of reading comments like this, eager for season 2 later:)
- Feb 22, 2009, 2:46:00 PM
- W.L.M. said...
I'm glad to see that you guys are starting to churn out the new story lines and everything for this season.
SOA made me feel so much better about The Shield ending. It is truly one of the best shows on TV.
I know you said that you can't really give anything away, but are there going to be any new interesting characters introduced this season?
- Feb 23, 2009, 12:36:00 AM
SoA just aired in Norway. Having seen all of season 1 already, I cannot wait to see what season 2 brings. Great work. Thanks, man.
Keep the rubber side down.
- Feb 23, 2009, 10:33:00 AM
- Justin said...
I'm interested in where Happy is going to come down in all of this. While everyone has done dirt, he's the heavy hitter that commands respect from everyone. I'll be interested to know whom he gives his allegiance to.
I still think Kip/Prospect is a Fed.
Really looking forward to the second season sir - every time I see a Bandido roll by I wonder "When does Season 2 start again?"
- Feb 23, 2009, 4:15:00 PM
- Kaz_NEETS said...
Great news. Looking forward to season two.
I also applaud FX for knowing what a great show/team they have in SoA. It works for everyone.
Thanks for the update Kurt.
- Feb 25, 2009, 10:16:00 AM
- Aaliyah said...
Thanks for the update it was so needed. It is giving me food for thought...I'm looking forward to season two.
In the meantime I'll rewatch episodes of season one to see if I can figure out where you and the other writer might go.
- Feb 27, 2009, 10:56:00 AM
- Kurt Yaeger said...
This isn't really for posting, but alas, here goes.
My name is Kurt Yaeger and I am a SAG actor, who happens to be an amputee. I am represented by Diverse Talent Group and Managed by Marv Douer and Associates. I am contacting you because I love the show "Sons of Anarchy" and by coincidence I am a very experienced motorcyclist (former professional x-games athlete).
I am trying to get in touch with the producers, directors and writers (in this case you) of the show because I have something, as an actor, uniquely suited for this show. Every single motorcycle club/gang has had one, if not more, of it's members loose a limb from a motorcycle accident. That story could be told for the first time, in dramatic fashion, with no CG costs to the show.
I have no limp, no noticeable gait issues, nothing that would indicate I am missing a limb. See my reel on my website www.kurtyaeger.com . Not one of the TV audience would know that I was missing a limb until the show revealed it (psychologically it would screw with them, creating amazing drama).
I know this is a long shot. However I am a legitimate working actor, who is very professional, who has had guest star roles, who has lead feature films (low budget SAG), and can ride motorcycles as well as anyone.
I've never tried to contact a show without my agent or manager before, but as I said, I know I could bring something quite amazing to the show.
All my best,
Kurt Yaeger
- Feb 27, 2009, 5:17:00 PM
- surrounded by carnivores said...
Someday I hope you might address how the male cast was chosen (I watched one FX clip with the casting director but it provided minimal information).
I am intrigued by the choice of Mr. Hunnam (Green Street Hooligans?). Did you have him in mind as your initial scripts were developed?
After reading that another actor was chosen for the HBO pilot but replaced by Mr. Perleman for FX I am left to wonder why. Mr. Perleman's previous roles are so very different from Clay but a season of SOA has revealed him to be perfect in the role.
The characters of Piney and "Elvis" are so different from the usual Cinema concept of M.C. members. And how did a Scotsman come to Charming and the club? Did you have a character like the one played so well by Mr. Flanagan always in mind?
I appreciate the remarkable variety of fine actors you have assembled
As I read fiction I am aware I have images of the characters (non actors, simply height, hair color, etc.)in mind. I am interested to know how a writer for film and tv makes these mental images real.
- Feb 28, 2009, 2:13:00 PM
- Outsider said...
I have angst from several backgrounds...
even though I was raised an atheist...
hard to shake.....but easy to slake
- Mar 2, 2009, 2:22:00 PM
- Joe Nobody said...
I was just wondering if you know when the SOA season one DVD comes out. I have looked everywhere (and emailed FX) to no avail.
- Apr 2, 2009, 2:06:00 PM


text:
National Bank of Kuwait (NBK), the largest Kuwaiti bank and the highest-rated in the Middle East, reported net profits of USD 499.3 million (KD 145.2 million) for the first half of 2010 compared with USD 433.4 million (KD 126.1) for the same period in 2009, an increase of 15%.
As of end June 2010, NBK Group's total assets reached USD 43.1 billion (KD 12.5 billion), whilst total shareholders' equity reached USD 6.4 billion (KD 1.9 billion). Profitability indicators remained strong with ROA reaching 2.3% and ROE 17.1%.
Ibrahim Dabdoub, NBK's Group Chief Executive Officer, said "The 15% net profit growth recorded by NBK in the first half of 2010 adds to the bank's achievements and its long record of weathering different crises. NBK's profit generation capacity remained very strong despite the ongoing consequences of the global financial crisis, sovereign debt distress in Europe and the low interest rate environment". Dabdoub added that the growth achieved reflects NBK's strong financial standing and validates the bank's ability to continue generating high returns to shareholders through core business activities inside and outside Kuwait.
Dabdoub attributed NBK's strong performance to its conservative strategy, sound risk management, strong corporate governance and stable management team. These attributes, along with NBK's clear strategic vision, helped the bank to maintain its strong financial position through different crises and earn international recognition, the latest being the "Best Bank in the Middle East" award for 2010 from Euromoney magazine, as well as being ranked as the safest bank in the Middle East for the past two years.
NBK's regional expansion strategy continues to pay off. "With net profit contribution in excess of 22%, NBK's regional and international operations continue to be very successful, especially in Egypt and Qatar," highlighted Dabdoub. The Bank's successful move into the Islamic banking space through the acquisition of a strategic stake in Boubyan Bank should further enhance NBK's domestic market position.
NBK continues to enjoy the widest banking presence in Kuwait with 71 branches, which together with its growing international representation add up to 177 branches worldwide. NBK's international presence spans many of the world's leading financial centers including London, Paris, Geneva, New York and Singapore, as well as China (Shanghai) and Vietnam (Ho Chi Minh City). Meanwhile, the regional coverage extends to Lebanon, Jordan, Iraq, Egypt, Bahrain, Qatar, Saudi Arabia, the UAE, and Turkey.
NBK has consistently been awarded the highest credit rating of all banks in the region by Moody's, Standard & Poor's, and Fitch Ratings. The Bank's ratings are supported by its high capitalization, prudent lending policies, and its disciplined approach to risk management, in addition to its recognized and very stable management team.
© 2000 - 2020 Al Bawaba (www.albawaba.com)


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There would seem to be no real sign yet of any progress towards home.
Harry has also been very unfortunate with leave. I had assumed that he had a leave in the summer of 1917, but from his letters it's clear that he went from his leave at the end of basic training, in the Spring of 1917, right through to September 1918. Now he has completed another fifteen months without a break. So much for the entitlement of two weeks leave a year! If he'd stayed with the York & Lancasters, it would appear that he would have, at least, had a leave by now. BL
address now 1st Garrison
40843/ Royal Munster Fusiliers
IEF Italy
Just a line to let you know that I am alright and in good health. I don't think it is any good writing about getting home as their is no chance of getting home for Christmas it looks like being more like the middle of April before I get home. I might manage it a bit before. all the prisoners are going home this month so it will make it a bit better after Christmas for us all. I am very pleased to here that all are going on alright at home & keeping in good health. I hope you and Agnes have a Happy Christmas & New Year. I am sorry that I shall not be at home for it but I am in good health so that is something. Write as often as you can and let me know all the news and send me a newspaper or two. I will write again soon.
40843/ 1st Garr Batt
Royal Munster Fusiliers
D. Coy. 17 Hut


text:
In the United States and many other countries, one of the important markers of high academic standards is proper attribution (giving credit) for someone else's ideas, thoughts, words, or methods of scholarship. Proper credit should be given in both oral and written contexts. Proper credit is:
- When you use an actual sentence from a published article or unpublished essay, including print and digital material, you must put the sentence in quote marks and give a footnote or citation to indicate who said it. The citation should include full bibliographic information. (For further information about correct citation form, see Kate Turabian, A Manual for Writers of Term Papers, Theses and Dissertations.)
- When you paraphrase or summarize another person's ideas, you must give a footnote or citation to indicate whose ideas they are and where you got them. (Or, in lecturing, make clear from whose ideas you are drawing.)
- When you adopt a significant idea from someone else's work, you must give a footnote or citation to indicate where you got the idea.
- When you use a method developed by someone else, you must give a footnote or citation to indicate the source of the method.
When you fail to do this, it is considered plagiarism. Plagiarism can apply both to students and to faculty. Plagiarism is using someone else's ideas, thoughts, words, or methods of scholarship as if they were your own and without giving proper credit to that person. Plagiarism is considered wrong because (1) it is 'stealing' another person's ideas, methods, etc., and (2) it is 'lying' — representing something as your own when it is not yours. At PSR, as at many comparable graduate-level institutions, plagiarism is considered a serious offense.
- Plagiarism includes failing to give citations in the examples above.
- Plagiarism also includes copying another student's exam or part of an exam or essay.
It is not plagiarism when you indicate clearly that you are summarizing someone else's views in order to provide the context for an assessment or critique of those views, or to incorporate them into a larger project. In this case, you must indicate clearly that you are giving the views of someone else — e.g. by starting with "so-and-so argues that…" It is also not plagiarism to use a well-established idea that has been developed in multiple sources — e.g. to claim that God can be called "woman" as well as man is now sufficiently well established that it needs no attribution. Some phrases — e.g. "the personal is political" — are in such wide usage that sometimes we do not know where they originated; in such cases, it is acceptable to use them without attribution. However, the best scholarship will make every effort to give attribution where possible (e.g. to note that this phrase came from Robin Morgan).
Procedures and Penalties
Instances of suspected plagiarism will be reported to the Academic Dean (or, in the case where the Dean is suspected of plagiarism, to the President). Suspected plagiarism may be reported by either students or faculty. The Dean or President will assess the evidence and investigate in order to determine whether plagiarism has occurred.
PSR's Plagiarism Policy applies to all PSR students and PSR faculty, including but not limited to adjunct and visiting faculty. In all cases of reported plagiarism, source information of what was plagiarized (such as the name of the article, textbook, author, or the original source of ideas) must be submitted at the time the case is reported to the Dean's Office for at least one identified instance of plagiarism. Failure to comply with the policy may result in a re-evaluation of work accused of containing plagiarism by other PSR faculty and/or other administrators as assigned by the Dean or President.
In the Case of Students
When plagiarism has been substantiated, the faculty person shall inform the dean's office (every instance of substantiated plagiarism must be reported to the dean's office so that plagiarism can be monitored). The faculty member will discuss the plagiarism policy with the student and the student shall fail the assignment.
Additionally, the student will sign a letter stating that he/she has committed plagiarism, has received a warning, and is aware of the consequences. This letter will be re-signed each time the student has committed plagiarism. The letter will be kept in the student's file.
If, when reporting student plagiarism to the dean's office, it is discovered that it is the student's second attempt at plagiarism, the faculty member, dean, and student shall meet together. The student will fail the course.
If a student plagiarizes a third time, the student shall be immediately expelled from the school.
The student will not be allowed re-apply to PSR for one year at minimum. If the student re-applies after one year, all documentation regarding instances of plagiarism and the institutional actions taken will be included in the admissions file for review. If the student is re-admitted, and one more confirmed instance of plagiarism occurs after re-admission, the student will be expelled again permanently with no option of returning.
In the Case of Faculty
If allegations of plagiarism appear to be substantiated, the faculty member has been charged with "action justifying dismissal," and shall be dealt with according to the procedures described in the Faculty Manual. Faculty who serve on the Core Doctoral Faculty of the Graduate Theological Union are also subject to the plagiarism policies and procedures of the GTU.
Policy adopted by PSR Faculty September 2009
GTU Consortial Agreement Regarding Plagiarism
When a student from one GTU school is suspected of plagiarism in a course that the student is
taking at another GTU school, the following protocol will be followed:
1. The faculty member teaching the course will notify the dean of the faculty member's school that the student has been suspected of plagiarism.
2. The dean of the faculty member's school will notify the dean of the student's school that the student has been suspected of plagiarism.
3. The faculty member will follow the policy of his or her own school in regard to possible consequences within the context of the course (e.g., failing grade on the assignment, failing grade for the course, etc.).
4. The student's school will be responsible for following its own policy in regard to possible consequences beyond the context of the course (e.g., warning, academic probation, expulsion, etc.)Approved by the Council of Deans, April 2010


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That play was a perfect encapsulation of the career of Mark Sanchez and I'll explain why, but first check the this introduction to an article in the Star-Ledger about the play in question.
Brandon Moore didn't understand the particular fascination with this one turnover when the Jets committed five on the night.Seriously? Maybe it was just because he had his back to the play, but how can he not get the fascination over the play?
Down two scores quickly due to some unfortunate circumstances and with the game now on the line, the Jets needed to score on that drive - and hopefully score a touchdown. The question was "how does this offense respond?" Will they abandon the run with plenty of time remaining in the game, or keep their grinding mentality going and maybe put together a plodding drive to get them back in the game.
Of course we didn't even have time to answer that question.
Because according to Mark Sanchez he was wrong about the play he had just called. Here's Sanchez's postgame remarks.
I was thinking a different play in my head, (so it was) just a mental error there. So as soon as I realized there was nobody to hand it to I started to run toward the line and tried to cover it up and just get down, and I slid right into Brandon Moore. That was kind of an unfortunate deal there. I'm not a big believer in luck, but that was pretty unlucky. It was really too bad.I didn't see any quotes from Hilliard or what his read on the play was, so we'll take Sanchez at his word on this one for now. Of course, even if it was Hilliard's fault ...
Here's the real point and the real reason for the fascination on the play, Brandon ... again Sanchez's own words.
[It was unlucky] just sliding right into [Moore]. I was just trying to get down, the play was over, just say uncle and do the right thing. And go back, second-and-ten, and fight another down and then the thing came out, so you just have to take care of the ball.**EVEN ON A BUSTED PLAY, MARK SANCHEZ CAN'T EVEN CONCEDE DEFEAT CORRECTLY.**