Volume 7 Number 1 (Jan. 2012)
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JSW 2012 Vol.7(1): 25-32 ISSN: 1796-217X
doi: 10.4304/jsw.7.1.25-32

A Simple but Effective Maximal Frequent Itemset Mining Algorithm over Streams

Haifeng Li, Ning Zhang, and Zhixin Chen
School of Information, Central University of Finance and Economics, Beijing China

Abstract—Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper considers a simple but effective algorithm for mining maximal frequent itemsets over a stream landmark. We design a compact data structure named FP-FOREST to improve an state-of-the-art algorithm INSTANT; thus, itemsets can be compressed and the support counting can be effective performed. Our experimental results show our algorithm achieves a better performance in memory cost and running time cost.

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Cite:Haifeng Li, Ning Zhang, Zhixin Chen, "A Simple but Effective Maximal Frequent Itemset Mining Algorithm over Streams," Journal of Software vol. 9, no.1, pp. 25-32, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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