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|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. | |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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Version vom 15. August 2009, 16:51 Uhr
Mining Frequent Patterns with Counting Inference
Mining Frequent Patterns with Counting Inference
Veröffentlicht: 2000
Journal: SIGKDD Exploration Special Issue on Scalable Algorithms
Seiten: 71-80
Referierte Veröffentlichung
Kurzfassung
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.
Download: Media:2000_524_Bastide_Mining_Frequent_1.pdf
Forschungsgebiet