A fuzzy-based concept formation system for categorization and numerical clustering

Fuzzy-set theory is compatible with the basic premises of the prototype theory of concept representation. Concept formation is defined as a machine learning task that captures concepts through categorizing the observation of objects and also uses them in classifying future experiences. A reasonable computational model of concept formation must reflect the characteristics of human concept learning and categorization. In this paper, the design and implementation of a fuzzy-set based concept formation system (FUZZ) is presented. The main feature of the FUZZ is that the concept hierarchy is non-disjoint, in which an instance may belong to two categories in different memberships. An information-theoretic evaluation measure called category binding to direct searches in the FUZZ is proposed. The learning and classification algorithms of the FUZZ are also given. In order to examine FUZZ´s behavior, the results of some experiments are examined