Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications
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Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery from Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable s...
Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery from Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools, and then joining them together to solve the initial problem. The benefits of decomposition methodology in data mining include: increased performance (classification accuracy), conceptual simplification of the problem, enhanced feasibility with huge databases, clearer and more comprehensible results, reduced runtime by solving smaller problems and by using parallel/distributed computation, and the opportunity of using different techniques for individual sub-problems.
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