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Mining frequent patterns with counting inference

Abstract : 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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Contributor : Nicolas Pasquier <>
Submitted on : Monday, April 26, 2010 - 1:24:56 AM
Last modification on : Friday, April 10, 2020 - 4:12:05 PM
Long-term archiving on: : Tuesday, September 14, 2010 - 5:40:05 PM


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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, Association for Computing Machinery (ACM), 2000, 2 (2), pp.66-75. ⟨hal-00467750⟩



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