Knowledge discovery in fuzzy databases using attribute-oriented induction

In this paper we analyze an attribute-oriented data induction technique for discovery of generalized knowledge from large data repositories. We employ a fuzzy relational database as the medium carrying the original information, where the lack of precise information about an entity can be reflected via multiple attribute values, and the classical equivalence relation is replaced with relation of the fuzzy proximity. Following a well-known approach for exact data generalization in the ordinary databases [1], we propose three ways in which the original methodology can be successfully implemented in the environment of fuzzy databases. During our investigation we point out both the advantages and the disadvantages of the developed tactics when applied to mine knowledge from fuzzy tuples.