CMAR: Accurate and efficient classification based on multiple class-association rules

Wenmin Li, Jiawei Han, Jian Pei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted χ2 analysis using multiple strong association rules. Our extensive experiments on 26 databases from UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Pages369-376
Number of pages8
StatePublished - Dec 1 2001
Externally publishedYes
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: Nov 29 2001Dec 2 2001

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other1st IEEE International Conference on Data Mining, ICDM'01
Country/TerritoryUnited States
CitySan Jose, CA
Period11/29/0112/2/01

ASJC Scopus subject areas

  • Engineering(all)

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