Research and improvement on association rule algorithm based on FP-Growth

Authors:
Jingbo Yuan;Shunli Ding
Affiliations:
Institute of Information Management Technology and Application, Northeastern University at Qinhuangdao, Qinhuangdao, China;Dept. of Information Engineering, Environmental Management College of China, Qinhuangdao, China
Venue:
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Year:
2012

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Abstract

Association rules mining (ARM) is one of the most useful techniques in the field of knowledge discovery and data mining and so on. Frequent item sets mining plays an important role in association rules mining. Apriori algorithm and FP-growth algorithm are famous algorithms to find frequent item sets. Based on analyzing on an association rule mining algorithm, a new association rule mining algorithm, called HSP-growth algorithm, is presented to generate the simplest frequent item sets and mine association rules from the sets. HSP-growth algorithm uses Huffman tree to describe frequent item sets. The basic idea and process of the algorithm are described and how to affects association rule mining is discussed. The performance study and the experimental results show that the HSP-growth algorithm has higher mining efficiency in execution time and is more efficient than Apriori algorithm and FP-growth algorithm.