Main Article Content
Distributed Data Mining(DDM) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of extracting, interesting and previously unknown knowledge from very large real-world databases. Fuzzy Set Theory (FST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in Distributed data-based systems. One important concept related to FST is that of a fuzzy relation. In this paper we presented the current status of research on applying fuzzy set theory to DDM, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how fuzzy set and fuzzy set analysis can be effectively used to extract knowledge from large databases.
Keywords: Data mining, Data tables, Distributed Data Mining (DDM), Fuzzy sets, Fuzzy set analysis.
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