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Article Dans Une Revue SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining Année : 2000

Mining frequent patterns with counting inference

Résumé

In this paper, we propose the algorithm PASCAL which introduces a novel optimization of the well-known algorithm Apriori. This optimization is based on a new strategy called pattern counting inference that relies on the concept of key patterns. We show that the support of frequent non-key patterns can be inferred from frequent key patterns without accessing the database. Experiments comparing PASCAL to the three algorithms Apriori, Close and Max-Miner, show that PASCAL is among the most efficient algorithms for mining frequent patterns.
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Dates et versions

hal-00467750 , version 1 (26-04-2010)

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  • HAL Id : hal-00467750 , version 1

Citer

Yves Bastide, Rafik Taouil, Nicolas Pasquier, Gerd Stumme, Lotfi Lakhal. Mining frequent patterns with counting inference. SIGKDD explorations : newsletter of the Special Interest Group (SIG) on Knowledge Discovery & Data Mining, 2000, 2 (2), pp.66-75. ⟨hal-00467750⟩
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