Fuzzy versus quantitative association rules: a fair data-driven comparison.

Full 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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Author information

Author/s: Verlinde, Hannes (H); De Cock, Martine (M); Boute, Raymond (R);

Journal and publication information

Publication Type: Letter; Research Support, Non-U.S. Gov't

Journal: IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society (IEEE Trans Syst Man Cybern B Cybern), published in United States. (Language: eng)

Reference: 2006-Jun; vol 36 (issue 3) : pp 679-84

Dates: Created 2006/06/09; Completed 2006/07/07; Revised 2007/09/11;

PMID: 16761820, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

Comments and Corrections

CommentIn: IEEE Trans Syst Man Cybern B Cybern. 2007 Aug;37(4):1039-43. (PMID: 17702300)

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Decision Support Techniques
Fuzzy Logic
Information Storage and Retrieval - methods
Models, Statistical

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