Fuzzy knowledge generation method for data-mining problems

Authors:
Dmitry Kropotov;Vladimir Ryazanov;Dmitry Vetrov
Affiliations:
Situation Recognition Department, Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow, Russian Federation;Situation Recognition Department, Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow, Russian Federation;Situation Recognition Department, Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow, Russian Federation
Venue:
ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Year:
2006

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Abstract

Fuzzy sets have been widely used for solving data-mining problems during the last years. Another possible area of fuzzy methods application is automatic knowledge generation based on the set of precedents. This area is very important for artificial intelligence and machine learning theory. In this paper we suggest a new algorithm for fuzzy knowledge generation. It can find all significant rules with respect to wide range of reasonable criterion functions. Besides, the number of rules being generated is not high and their size is short thus simplifying decision interpretation by expert. We present the statistical criterion for knowledge quality estimation that provides high generalization ability. The theoretical results are complemented with the experimental evaluation.