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Regularity Analysis and its Applications in Data Mining

  • Sinh Hoa Nguyen
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 56)

Abstract

Abstract: Knowledge discovery is concerned with extraction of useful information from databases ([21]). One of the basic tasks of knowledge discovery and data mining is to synthesize the description of some subsets (concepts) of entities contained in databases. The patterns and/or rules extracted from data are used as basic tools for concept description. In this Chapter we propose a certain framework for approximating concepts. Our approach emphasizes extracting regularities from data. In this Chapter the following problems are investigated: (1) issues concerning the languages used to represent patterns; (2) computational complexity of problems in approximating concepts; (3) methods of identifying, optimal patterns. Data regularity is a useful tool not only for concept description. It is also indispensable for various applications like classification or decomposition. In this Chapter we present also the applications of data regularity to three basic problems of data mining: classification, data description and data decomposition.

Keywords

Similarity Relation Decision Table Tolerance Relation Decision Class Generalize Template 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2000

Authors and Affiliations

  • Sinh Hoa Nguyen
    • 1
    • 2
  1. 1.Institute of Computer Sciences Warsaw UniversityWarsawPoland
  2. 2.Polish-Japanese Institute of Information TechnologyWarsawPoland

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