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A counting mining algorithm of maximum frequent itemset based on matrix

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1 Author(s)
Haiwei Jin ; Coll. of Comput. & Inf. Eng., Zhejiang Gongshang Univ., Hangzhou, China

Mining frequent itemset is an important research topic in association rule area. There are two main kinds of Algorithm: Apriori Algorithm and FP- growth Algorithm and their varieties. Generating candidate itemset of Apriori and traversing tree nodes of FP-growth affect the efficiency of data mining. This paper puts forward the new simplified algorithm: eliminating and plotting blocks to the matrix with simply counting rows and columns, thus, to find out maximal frequent itemset. The experiment results show that the algorithm can improve mining efficiency.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on  (Volume:3 )

Date of Conference:

10-12 Aug. 2010