Massive Pruning for Building an Operational Set of Association Rules: Metarules for Eliminating Conflicting and Redundant Rules.

Martine Cadot 1 Alain Lelu 2, 3
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
3 KIWI - Knowledge Information and Web Intelligence
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Extracting a set of Association Rules (AR) is a common method for representing knowledge embedded in a database. As long as many authors have aimed at improving the individual quality of these rules, not so many have considered their global quality and cohesiveness: Our objective is to provide the user with a set of rules he/she may combine to reason with, a consistent set as regards to “common sense logic”. As local quality measures offer no warranty in this respect, we have defined patterns of major incoherencies and have associated metarules to them, resulting in a post-treatment cleaning phase for tracking down incoherencies and proposing corrections. We show that on the artificial Lucas0 database of the Causality Challenge [11], starting from 100 000 rules, we have reduced this rule set by three orders of magnitude, to 69 high-quality condensed rules embedding most of the structure designed by the challenge organizers.
Type de document :
Communication dans un congrès
Andrew Kusiak, Sang-goo Lee. International Conference on Information, Process, and Knowledge Management - eKnow09, Feb 2009, Cancun, Mexico. pp.90-98, 2009
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https://hal.inria.fr/inria-00337067
Contributeur : Martine Cadot <>
Soumis le : mercredi 5 novembre 2008 - 23:15:30
Dernière modification le : jeudi 15 février 2018 - 08:48:10

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  • HAL Id : inria-00337067, version 1

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Martine Cadot, Alain Lelu. Massive Pruning for Building an Operational Set of Association Rules: Metarules for Eliminating Conflicting and Redundant Rules.. Andrew Kusiak, Sang-goo Lee. International Conference on Information, Process, and Knowledge Management - eKnow09, Feb 2009, Cancun, Mexico. pp.90-98, 2009. 〈inria-00337067〉

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