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

Hannes Verlinde; Martine De Cock; Raymond Boute

doi:10.1109/tsmcb.2005.860134

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

Hannes Verlinde·Martine De Cock·Raymond Boute

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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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Algorithms Artificial Intelligence Computer Simulation Databases, Factual Decision Support Techniques Fuzzy Logic Information Storage and Retrieval Models, Statistical Letter Research Support, Non-U.S. Gov't

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Hannes Verlinde, Martine De Cock, Raymond Boute. 2006. Fuzzy versus quantitative association rules: a fair data-driven comparison.. https://doi.org/10.1109/tsmcb.2005.860134

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