Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

Abstract:

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Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

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Periodical:

Edited by:

Xiangdong Zhang, Hongnan Li, Xiating Feng and Zhihua Chen

Pages:

2910-2913

DOI:

10.4028/www.scientific.net/AMM.256-259.2910

Citation:

J. Tan "Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm", Applied Mechanics and Materials, Vols. 256-259, pp. 2910-2913, 2013

Online since:

December 2012

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$35.00

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