Paper Title:
New Policy of Maximal Frequent Itemsets in Data Stream Mining
  Abstract

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

  Info
Periodical
Edited by
Zhenyu Du and Bin Liu
Pages
118-122
DOI
10.4028/www.scientific.net/AMM.26-28.118
Citation
C. H. Xu, C. H. Ju, "New Policy of Maximal Frequent Itemsets in Data Stream Mining", Applied Mechanics and Materials, Vols. 26-28, pp. 118-122, 2010
Online since
June 2010
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Price
$32.00
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