Deriving class association rules based on levelwise subspace clustering

Authors:
Takashi Washio;Koutarou Nakanishi;Hiroshi Motoda
Affiliations:
I.S.I.R., Osaka University, Ibaraki City, Osaka, Japan;I.S.I.R., Osaka University, Ibaraki City, Osaka, Japan;I.S.I.R., Osaka University, Ibaraki City, Osaka, Japan
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

Most approaches of Class Association Rule (CAR) based classification have not intensively addressed the classification of instances including numeric attributes. In this paper, a levelwise subspace clustering method deriving hyper-rectangular clusters is proposed to efficiently provide quantitative, interpretative and accurate CARs.