Mining association rules on significant rare data using relative support

Authors:
Hyunyoon Yun;Danshim Ha;Buhyun Hwang;Keun Ho Ryu
Affiliations:
Department of Computer Science, Chonnam National University, Kwangju, South Korea;LG Electronics Inc., Seoul, South Korea;Department of Computer Science, Chonnam National University, Kwangju, South Korea;School of Electrical & Computer Engineering, Chungbuk National University, Cheongju 361-763, South Korea
Venue:
Journal of Systems and Software
Year:
2003

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Abstract

Recently, data mining, a technique to analyze the stored data in large databases to discover potential information and knowledge, has been a popular topic in database research. In this paper, we study the techniques discovering the association rules which are one of these data mining techniques. And we propose a technique discovering the association rules for significant rare data that appear infrequently in the database but are highly associated with specific data. Furthermore, considering these significant rare data, we evaluate the performance of the proposed algorithm by comparing it with other existing algorithms for discovering the association rules.