A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> The Knowledge Discovery Process <s> The large real-world datasets now commonly tackled by machine learning algorithms are often described in terms of attributes whose values are real numbers on some continuous interval, rather than being taken from a small number of discrete values. Many algorithms are able to handle continuous attributes, but learning requires far more CPU time than for a corresponding task with discrete attributes. This paper describes how continuous attributes can be converted economically into ordered discrete attributes before being given to the learning system. Experimental results from a wide variety of domains suggest this change of representation does not often result in a significant loss of accuracy (in fact it sometimes significantly improves accuracy), but offers large reductions in learning time, typically more than a factor of 10 in domains with a large number of continuous attributes. <s> BIB001 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> The Knowledge Discovery Process <s> An electric shock weapon is disclosed for use by officers in maintaining order at public gatherings and for other crowd control applications. The invention is embodied in an electrified stick or in an electrified garment worn by the user. The device comprises at least one pair of exposed conductors which are connected across a high voltage power supply and adapted to be simultaneously engaged with the anatomy of a would-be assailant and thus to ward off or cause retreat of such person. In order to give warning of the character of the device, and to lend a menacing appearance, a sparking device is disposed on the weapon and connected across the high voltage power supply to provide a spark discharge. In the embodiment of the invention in an electrified stick a grip portion is provided at the rear end and a slidable insulating sleeve is disposed over a portion of the conductors and movable to the forward end of the stick to provide an auxiliary gripping portion for two-handed use of the stick. The high voltage power supply preferably comprises a capacitor discharge system comprising a battery energized transistor inverter and a voltage doubler connected across a storage capacitor, with a self-switching discharge circuit connected with an induction coil which in turn is connected with the exposed conductors on the device. A timing circuit and a silicon controlled rectifier are employed in the self-switching discharge circuit. <s> BIB002 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> The Knowledge Discovery Process <s> We present a method for discovering informative patterns from data. With this method, large databases can be reduced to only a few representative data entries. Our framework encompasses also methods for cleaning databases containing corrupted data. Both on-line and off-line algorithms are proposed and experimentally checked on databases of handwritten images. The generality of the framework makes it an attractive candidate for new applications in knowledge discovery. <s> BIB003 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> The Knowledge Discovery Process <s> Database mining is the process of finding previously unknown rules and relations in large databases. Often, several database mining techniques must be used cooperatively in a single application. In this paper we present the Recon database mining framework, which integrates three database mining techniques: rule induction, rule deduction, and data visualization. <s> BIB004 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> The Knowledge Discovery Process <s> Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing.Dorian Pyle corrects this imbalance. A twenty-five-year veteran of what has become the data mining industry, Pyle shares his own successful data preparation methodology, offering both a conceptual overview for managers and complete technical details for IT professionals. Apply his techniques and watch your mining efforts pay off-in the form of improved performance, reduced distortion, and more valuable results.On the enclosed CD-ROM, you'll find a suite of programs as C source code and compiled into a command-line-driven toolkit. This code illustrates how the author's techniques can be applied to arrive at an automated preparation solution that works for you. Also included are demonstration versions of three commercial products that help with data preparation, along with sample data with which you can practice and experiment. * Offers in-depth coverage of an essential but largely ignored subject.* Goes far beyond theory, leading you-step by step-through the author's own data preparation techniques.* Provides practical illustrations of the author's methodology using realistic sample data sets.* Includes algorithms you can apply directly to your own project, along with instructions for understanding when automation is possible and when greater intervention is required.* Explains how to identify and correct data problems that may be present in your application.* Prepares miners, helping them head into preparation with a better understanding of data sets and their limitations. <s> BIB005
The application of a data mining algorithm to a data set can be considered the core step of a broader process, often called the knowledge discovery process  . In addition to the data mining step itself, this process also includes several other steps. For the sake of simplicity, these additional steps can be roughly categorized into data preprocessing and discovered-knowledge postprocessing. We use the term data preprocessing in a general sense, including the following steps (among others) BIB005 : (a) Data Integration -This is necessary if the data to be mined comes from several different sources, such as several departments of an organization. This step involves, for instance, removing inconsistencies in attribute names or attribute value names between data sets of different sources. (b) Data Cleaning -It is important to make sure that the data to be mined is as accurate as possible. This step may involve detecting and correcting errors in the data, filling in missing values, etc. Data cleaning has a strong overlap with data integration, if this latter is also performed. It is often desirable to involve the user in data cleaning and data integration, so that (s)he can bring her/his background knowledge into these tasks. Some data cleaning methods for data mining are discussed in BIB003 , BIB004 . (c) Discretization -This step consists of transforming a continuous attribute into a categorical (or nominal) attribute, taking on only a few discrete values -e.g., the real-valued attribute Salary can be discretized to take on only three values, say "low", "medium", and "high". This step is particularly required when the data mining algorithm cannot cope with continuous attributes. In addition, discretization often improves the comprehensibility of the discovered knowledge BIB001 , BIB002 . (d) Attribute Selection -This step consists of selecting a subset of attributes relevant for classification, among all original attributes. It will be discussed in subsection 2.3.1. Discovered-knowledge postprocessing usually aims at improving the comprehensibility and/or the interestingness of the knowledge to be shown to the user. This step may involve, for instance, the selection of the most interesting rules, among the discovered rule set. This step will be discussed in subsection 2.3.2. Note that the knowledge discovery process is inherently iterative, as illustrated in Fig. 1 . As can be seen in this figure, the output of a step can be not only sent to the next step in the process, but also sent -as a feedback -to a previous step. Note that a number of data mining algorithms, particularly rule induction ones, already perform a kind of attribute selection when they discover a rule containing just a few attributes, rather than all attributes. However, in this section we are interested in attribute selection as a preprocessing step for the data mining algorithm. Hence, we first select an attribute subset and then give only the selected attributes for the data mining algorithm. The motivation for this kind of preprocessing is the fact that irrelevant attributes can somehow "confuse" the data mining algorithm, leading to the discovery of inaccurate or useless knowledge  . Considering an extreme example, suppose we try to predict whether the credit of a customer is good or bad, and suppose that the data set includes the attribute Customer_Name. A data mining algorithm might discover too specific rules of the form: IF (Customer_Name = "a_specific_name") THEN (Credit = "good"). This kind of rule has no predictive power. Most likely, it covers a single customer and cannot be generalized to other customers. Technically speaking, it is overfitting the data. To avoid this problem, the attribute Customer_Name (and other attributes having a unique value for each training example) should be removed in a preprocessing step. Attribute selection methods can be divided into filter and wrapper approaches. In the filter approach the attribute selection method is independent of the data mining algorithm to be applied to the selected attributes. By contrast, in the wrapper approach the attribute selection method uses the result of the data mining algorithm to determine how good a given attribute subset is. In essence, the attribute selection method iteratively generates attribute subsets (candidate solutions) and evaluates their qualities, until a termination criterion is satisfied. The attribute-subset generation procedure can be virtually any search method. The major characteristic of the wrapper approach is that the quality of an attribute subset is directly measured by the performance of the data mining algorithm applied to that attribute subset. The wrapper approach tends to be more effective than the filter one, since the selected attributes are "optimized" for the data mining algorithm. However, the wrapper approach tends to be much slower than the filter approach, since in the former a full data mining algorithm is applied to each attribute subset considered by the search. In addition, if we want to apply several data mining algorithms to the data, the wrapper approach becomes even more computationally expensive, since we need to run the wrapper procedure once for each data mining algorithm.