Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix

Abstract:

Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

• Periodical: Applied Mechanics and Materials (Volumes 571-572)
• Pages: 57-62
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.571-572.57
• Online since: June 2014
• Authors: Si Hui Shu, Zi Zhi Lin*
• Keywords: Apriori Algorithm, Association Rule, Compression Matrix; , Data Mining (DM), Maximum Frequent Itemsets

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