Paper Title:
Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy
  Abstract

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, 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
$32.00
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