CCCS: A top-down associative classifier for imbalanced class distribution

Bavani Arunasalam, Sanjay Chawla

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

39 Citations (Scopus)

Abstract

In this paper we propose CCCS, a new algorithm for classification based on association rule mining. The key innovation in CCCS is the use of a new measure, the "Complement Class Support (CCS)" whose application results in rules which are guaranteed to be positively correlated. Furthermore, the anti-monotonic property that CCS possesses has very different semantics vis-a-vis the traditional support measure. In particular, "good" rules have a low CCS value. This makes CCS an ideal measure to use in conjunction with a top-down algorithm. Finally, the nature of CCS allows the pruning of rules without the setting of any threshold parameter! To the best of our knowledge this is the first threshold-free algorithm in association rule mining for classification.

Original languageEnglish
Title of host publicationKDD 2006
Subtitle of host publicationProceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages517-522
Number of pages6
Publication statusPublished - 16 Oct 2006
EventKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Philadelphia, PA, United States
Duration: 20 Aug 200623 Aug 2006

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2006

Other

OtherKDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CityPhiladelphia, PA
Period20/8/0623/8/06

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Keywords

  • Association Rules Mining
  • Classification
  • Imbalanced Data sets
  • Parameter-free mining

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Arunasalam, B., & Chawla, S. (2006). CCCS: A top-down associative classifier for imbalanced class distribution. In KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 517-522). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006).