A Fiizzy-Based Concept Formation System for Categorizatioii and Numerical Clustering
نویسندگان
چکیده
Fuzzy-set theory is compatible with the basic, premises of the prot,oty,pe theory of concept representation. Concept formation is defined as a machine learning task tha t captures concepts through categorizing the observation of objects and also uses them in classifying future experiences. A reasonable computational model of concept format,ion must reflect the characteristics of human concept learning and cat,egoriza.tion. In this paper. the design and implciiientation of a fuzzy-set based concept formation system (FUZZ) is presented. The main feature of the F U Z Z is tha t the concept hierarchy is non-disjoint, in which an !nstance may belong. t o two categories in different memberships. An information-theoretic evaluation measure called categor binding to direct searches in the FUZZ is pro osed. The learning and classilcatjon algorithms of tslie FUZZ are also given. Pn order bo esamine FUZZ's behavior, the results of some esper imenh are examined.
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