Regularity Analysis and its Applications in Data Mining
Sinh Hoa Nguyen · Studies in fuzziness and soft computing · 2000
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. 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.