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Proceedings Paper

Efficiently mining maximal frequent patterns: fast-miner
Author(s): Michael J. Dewsnip; Malika Mahoui
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Paper 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.

Paper Details

Date Published: 27 March 2001
PDF: 6 pages
Proc. SPIE 4384, Data Mining and Knowledge Discovery: Theory, Tools, and Technology III, (27 March 2001); doi: 10.1117/12.421069
Show Author Affiliations
Michael J. Dewsnip, Univ. of Waikato (New Zealand)
Malika Mahoui, Univ. of Waikato (New Zealand)

Published in SPIE Proceedings Vol. 4384:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology III
Belur V. Dasarathy, Editor(s)

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