A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> Abstract XGobi is a data visualization system with state-of-the-art interactive and dynamic methods for the manipulation of views of data. It implements 2-D displays of projections of points and lines in high-dimensional spaces, as well as parallel coordinate displays and textual views thereof. Projection tools include dotplots of single variables, plots of pairs of variables, 3-D data rotations, various grand tours, and interactive projection pursuit. Views of the data can be reshaped. Points can be labeled and brushed with glyphs and colors. Lines can be edited and colored. Several XGobi processes can be run simultaneously and linked for labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns can be examined; multiple imputations can be given to XGobi for rapid visual diagnostics. XGobi includes an extensive online help facility. XGobi can be integrated in other software systems, as has been done for the data analysis language S, the geographic information system... <s> BIB001 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> Method and system for finding targets within visual data, comprising receiving target object information. Generating a set of target object semantic attributes from the target object information. Identifying a plurality of candidate objects within visual data. Generating a set of low-level feature descriptors from the visual data for each candidate object. Generating from the set of low-level feature descriptors a set of candidate semantic attributes for each candidate object within the visual data. Identifying one or more portions of the visual data containing a candidate object, from the plurality of candidate objects, having a set of candidate object semantic attributes that match the set of target object semantic attributes. Providing an output indicating the identified one or more portions of the visual data. <s> BIB002 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> Plots, probability plots and regression tests tests using moments other tests for univariate normality goodness of fit tests testing for outliers in univariate samples power comparisons for univariate tests for normality testing for normalitywith censored data assessing multivariate normality testing for multivariate outliers testing for normal mixtures robust methods computational methods and issues. Appendices: data sets used in examples critical values for tests. <s> BIB003 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> GODIVA2 is a dynamic website that provides visual access to several terabytes of physically distributed, four-dimensional environmental data. It allows users to explore large datasets interactively without the need to install new software or download and understand complex data. Through the use of open international standards, GODIVA2 maintains a high level of interoperability with third-party systems, allowing diverse datasets to be mutually compared. Scientists can use the system to search for features in large datasets and to diagnose the output from numerical simulations and data processing algorithms. Data providers around Europe have adopted GODIVA2 as an INSPIRE-compliant dynamic quick-view system for providing visual access to their data. <s> BIB004 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003. <s> BIB005 </s> A survey on pre-processing techniques: Relevant issues in the context of environmental data mining <s> Data visualization <s> Unique prospective on the big data analytics phenomenon for both business and IT professionalsThe availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability.The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics.Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.)Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insightsExplores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more. <s> BIB006
Visualization is a powerful strategy for leveraging the visual orientation of sighted human beings. Sighted humans are extraordinarily good at recognizing visual patterns, trends and anomalies; these skills are valuable at all stages of the KDD  BIB002 . Many of the preprocessing detection issues, like outlier detection, normality assessment or linearities, randomness of missing values or multimodalities, can be assessed through visualization techniques  . Graphs commonly used for classical exploratory visualization, like boxplots, histograms, time series plots or scatter plots perform poorly considering the great number of variables involved in ESs datasets, along with their complex interrelations or spatio-temporal references. Thus, more sophisticated visualization methods are required, as for example: • Distributional plots, • Three, four, and five dimensional plots (color and symbols may be used to represent the higher dimensions), • Density plots, • Dimension scaling, like log scales, • Rotary frames, • Animation and interactive graphs, • Geo-referenced visualizations and maps. Most data mining packages, such as Weka BIB005 , TABLEAU  , QlikTech BIB006 , include visualization tools, while more advanced features are provided with wide-spread tools such as Matlab, a dedicated data language such as IDL  or the CommonGIS tool  . Dedicated interactive visualization tools are also available, such as GGobi BIB001 or GODIVA2 BIB004 . Visual representations are extremely effective at preprocessing stage to identify the basic pre-processing issues to deal with. For example, some data errors or existence of outliers or structural missing data might be visible on certain visualizations. Also, some KDD methods, mainly those coming from classical statistics, usually involve some technical assumptions that must held on the target domain to guarantee the validity of the model. This is the case of linear regression, for example, in which independent observations, non-correlated variables, conditional normality of the response variable and linear relationship between the response variable and the explanatory variables are required. Some of these properties can be assessed on data just before the analysis and can help to decide, for instance, whether the linear regression is the proper approach for the target model or other alternatives like artificial neural nets can perform better on our data. Part of these properties can be assessed using visualization tools, like assessing normality by means of histograms or Q-Q plots, or the normal probability plot and the Henri line BIB003 ; or Scatter plots can help to see independence or linearities. This is not exclusive for statistical methods. Inductive machine learning methods, like Decision Trees for example, require balanced data sets to provide reliable results, or certain clustering methods require constant data density. These kinds of issues are easily observable over some charts or scatterplots and become extremely difficult to evaluate in a numerical way. Although visual assessment often is very efficient in these cases, when possible, tests can be introduced