Verlinde H, De Cock M, Boute R. Fuzzy versus quantitative association rules: a fair data-driven comparison.
ACTA ACUST UNITED AC 2006;
36:679-84. [PMID:
16761820 DOI:
10.1109/tsmcb.2005.860134]
[Citation(s) in RCA: 38] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.
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