Efficiently mining maximal frequent patterns: fast-miner

Michael J. Dewsnip; Malika Mahoui

doi:10.1117/12.421069

27 March 2001 Efficiently mining maximal frequent patterns: fast-miner

Michael J. Dewsnip, Malika Mahoui

Proceedings Volume 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III; (2001) https://doi.org/10.1117/12.421069
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

Abstract

The problem of finding maximal patterns in databases has been an intensive search area in recent years. The Max-Miner algorithm has been presented as an efficient pattern-mining algorithm which extracts maximal frequent itemsets from databases. Although Max-Miner produces only the set of maximal patterns, it does generate many candidate maximal patterns that need to be discarded at the end of the mining process. In this paper, we propose a set of enhancements to Max-Miner algorithm in order to address this issue. The new version of the algorithm uses new pruning strategies combined with adequate data structures to both speed up the process of counting the support of itemsets and avoid the processing of a larger number of non-maximal patterns. These new features translate directly into a considerable gain in performance. The proposed algorithm also has important features such as requiring a constant number of database passes, and supporting a pipeline structure which enables to output patterns as soon as they are identified as maximal patterns.

Citation Download Citation

Michael J. Dewsnip and Malika Mahoui "Efficiently mining maximal frequent patterns: fast-miner", Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); https://doi.org/10.1117/12.421069

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