Descriptive data mining

This section will therefore focus on the aims and tasks of data mining and refer to the methods where applicable. A thorough description of data mining is given in Ref. [20]. 

Data mining can fulfill various different tasks such as classification, clustering and similarity detection, prediction, estimation, or description retrieval, which are described in Sections 9.8.1-9.8.5. 

A very important data mining task is the discovery of characteristic descriptions for subsets of data, which characterize its members and distinguish it from other subsets. Descriptions can, for example, be the output of statistical methods like average or variance. 

Pattern Recognition. The application of computers to build descriptive or predictive models (i.e., find patterns) of information from input datasets. The techniques of pattern recognition overlap those used in statistics, chemometrics, and data mining, and include data display, description, and reduction, unsupervised methods such as cluster analy-... 

Unsupervised Data Mining. Searching large volumes of data for hidden descriptive relationships. Unlike supervised data mining, no response variables are used. The techniques used include various display and data reduction methods, as well as cluster analysis and association analysis. 

Sklorz, S., Jarke, M. MIDAS Explorative data mining in business applications. a project description. In Herzog, O. (ed.) KI 1998. LNCS, vol. 1504, Springer, Heidelberg (1998)... 

Based on the emphasis on in silico modeling or SAR extraction, the tasks of data mining in these areas are different. Regarding SAR extraction and identification, the task is to derive a comprehensive description of the SAR in the data. With regard to virtual screening and model building, the task is clearly to establish predictive models. We will briefly review methods and applications in both task areas. 

Any form of statistical methods exceeding simple descriptive statistics, such as statistical tests, correlations, regression analysis, factorial or cluster analysis, data mining techniques,. .. ... 

Below is a brief summary of recent advances in the field of data mining. A description of several advances specific to machine learning can be found in AI Magazine (Dietterich, 1997). 

Statistics is a collection of methods of enquiry used to gather, process, or interpret quantitative data. The two main functions of Statistics are to describe and summarize data and to make inferences about a larger population of which the data are representative. These two areas are referred to as Descriptive and Inferential Statistics, respectively both areas have an important part to play in Data Mining. Descriptive Statistics provides a toolkit of methods for data summarization while Inferential Statistics is more concerned with data analysis. 

Non-logic-based matching applies techniques such as graph matching, data mining, linguistics, or content-based information retrieval to exploit semantics that are either commonly shared (in XML namespaces) or implicit in patterns or relative frequencies of terms in service descriptions ... 

There are various tools for risk prediction, ranging from complex mathematical models to a less complicated Event Tree Analysis. The following section provides a description of two types of risk prediction tools Data Mining and Failure Mode Effect Analysis (FMEA) ... 

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