Mining quantitative association rules in large relational tables
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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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.