Searching interesting association rules based on evolutionary computation

Authors:
Guangfei Yang;Yanzhong Dang;Shingo Mabu;Kaoru Shimada;Kotaro Hirasawa
Affiliations:
Institute of Systems Engineering, Dalian University of Technology, China;Institute of Systems Engineering, Dalian University of Technology, China;Graduate School of IPS, Waseda University, Japan;Graduate School of IPS, Waseda University, Japan;Graduate School of IPS, Waseda University, Japan
Venue:
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Year:
2011

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Abstract

In this paper, we propose an evolutionary method to search interesting association rules. Most of the association rule mining methods give a large number of rules, and it is difficult for human beings to deal with them. We study this problem by borrowing the style of a search engine, that is, searching association rules by keywords. Whether a rule is interesting or not is decided by its relation to the keywords, and we introduce both semantic and statistical methods to measure such relation. The mining process is built on an evolutionary approach, Genetic Network Programming (GNP). Different from the conventional GNP based association rule mining method, the proposed method pays more attention to generate the GNP individuals carefully, which will mine interesting association rules efficiently.