A Classification Method Based on Subspace Clustering and Association Rules

摘要

Class Association Rule (CAR) based classification is a growing topic in recent datamining study for its high interpretability and accuracy. However, most of the approaches have not intensively addressed the classification of instances including numeric attributes. In this paper, a levelwise subspace clustering deriving hyper-rectangular clusters is proposed to efficiently provide quantitative, interpretative and accurate CARs. Significant performance of the proposed approach has been demonstrated through the tests on UCI repository data.