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Mining fuzzy quantitative association rules
Author(s) -
Subramanyam R.B.V.,
Goswami A.
Publication year - 2006
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2006.00402.x
Subject(s) - computer science , data mining , fuzzy logic , association rule learning , neuro fuzzy , fuzzy set operations , artificial intelligence , representation (politics) , fuzzy classification , visualization , machine learning , fuzzy set , fuzzy control system , politics , political science , law
The concept of fuzzy sets is one of the most fundamental and influential tools in the development of computational intelligence. In this paper the fuzzy pincer search algorithm is proposed. It generates fuzzy association rules by adopting combined top‐down and bottom‐up approaches. A fuzzy grid representation is used to reduce the number of scans of the database and our algorithm trims down the number of candidate fuzzy grids at each level. It has been observed that fuzzy association rules provide more realistic visualization of the knowledge extracted from databases.