Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

Abstract:

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Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.

Info:

Periodical:

Edited by:

Zhenyu Du and Bin Liu

Pages:

113-117

DOI:

10.4028/www.scientific.net/AMM.26-28.113

Citation:

P. S. Chen and C. H. Xu, "Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy", Applied Mechanics and Materials, Vols. 26-28, pp. 113-117, 2010

Online since:

June 2010

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Price:

$35.00

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