NDLI: TFP: an efficient algorithm for mining top-k frequent closed itemsets

Content Provider	IEEE Xplore Digital Library
Author	Jianyong Wang Han, J. Lu, Y. Tzvetkov, P.
Copyright Year	1989
Abstract	Frequent itemset mining has been studied extensively in literature. Most previous studies require the specification of a min/spl I.bar/support threshold and aim at mining a complete set of frequent itemsets satisfying min/spl I.bar/support. However, in practice, it is difficult for users to provide an appropriate min/spl I.bar/support threshold. In addition, a complete set of frequent itemsets is much less compact than a set of frequent closed itemsets. In this paper, we propose an alternative mining task: mining top-k frequent closed itemsets of length no less than min/spl I.bar/l, where k is the desired number of frequent closed itemsets to be mined, and min/spl I.bar/l is the minimal length of each itemset. An efficient algorithm, called TFP, is developed for mining such itemsets without mins/spl I.bar/support. Starting at min/spl I.bar/support = 0 and by making use of the length constraint and the properties of top-k frequent closed itemsets, min/spl I.bar/support can be raised effectively and FP-Tree can be pruned dynamically both during and after the construction of the tree using our two proposed methods: the closed node count and descendant/spl I.bar/sum. Moreover, mining is further speeded up by employing a top-down and bottom-up combined FP-Tree traversing strategy, a set of search space pruning methods, a fast 2-level hash-indexed result tree, and a novel closed itemset verification scheme. Our extensive performance study shows that TFP has high performance and linear scalability in terms of the database size.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	652
Ending Page	663
Page Count	12
File Size	759424
File Format	PDF
ISSN	10414347
Volume Number	17
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Itemsets Scalability Association rules Data structures Transaction databases Explosions mining methods and algorithms. Index Terms- Data mining frequent itemset association rules
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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