On the Use of Fuzzy Logic in Data Mining

In this chapter we describe some basic concepts from fuzzy logic and how their applicability to Data Mining. First we discuss some basic terms from fuzzy set theory and fuzzy logic. Then, we provide examples that show how fuzzy sets and fuzzy logic can be applied best to discover knowledge from a given database.

[1]  Keith C. C. Chan,et al.  Mining fuzzy association rules , 1997, CIKM '97.

[2]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[3]  Gregory Piatetsky-Shapiro,et al.  Knowledge Discovery in Databases: An Overview , 1992, AI Mag..

[4]  Tzung-Pei Hong,et al.  A fuzzy data mining algorithm for quantitative values , 1999, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410).

[5]  Tzung-Pei Hong,et al.  Mining association rules from quantitative data , 1999, Intell. Data Anal..

[6]  A. Kandel,et al.  FUZZY MEASURE FOR SIMILARITY OF NUMERICAL VECTORS , 1997 .

[7]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[8]  Wai-Ho Au,et al.  FARM: a data mining system for discovering fuzzy association rules , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).