Information-theoretic fuzzy approach to data reliability and data mining

A novel, information-theoretic fuzzy approach to discovering unreliable dat a in a relational database is presented. A multilevel information-theoretic connectionist network is constructed to evaluate activation functions of p artially reliable database values. The degree of value reliability is defin ed as a fuzzy measure of difference between the maximum attribute activatio n and the actual value activation. Unreliable values can be removed from th e database or corrected to the values predicted by the network. The method is applied to a real-world relational database which is extended to a fuzzy relational database by adding fuzzy attributes representing reliability de grees of crisp attributes. The highest connection weights in the network ar e translated into meaningful if, then rules. This work aims at improving re liability of data in a relational database by developing a framework for di scovering, accessing and correcting lowly reliable data. (C) 2001 Elsevier Science B.V. All rights reserved.