Basic Principles of Data Mining

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Author(s): Karl-Ernst Erich Biebler (Ernst-Moritz-Arndt-University, Germany)
Copyright: 2009
Pages: 24
Source title: Social Implications of Data Mining and Information Privacy: Interdisciplinary Frameworks and Solutions
Source Author(s)/Editor(s): Ephrem Eyob (Virginia State University, USA)
DOI: 10.4018/978-1-60566-196-4.ch015

Keywords: Data Mining / Data Mining and Databases / Information Science Reference / Library & Information Science

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Abstract

This chapter gives a summary of data types, mathematical structures, and associated methods of data mining. Topological, order theoretical, algebraic, and probability theoretical mathematical structures are introduced. The n-dimensional Euclidean space, the model used most for data, is defined. It is executed briefly that the treatment of higher dimensional random variables and related data is problematic. Since topological concepts are less well known than statistical concepts, many examples of metrics are given. Related classification concepts are defined and explained. Possibilities of their quality identification are discussed. One example each is given for topological cluster and for topological discriminant analyses.

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