DualMiner: A dual-pruning algorithm for itemsets with constraints

Cristian Bucila, Johannes Gehrke, Daniel Kifer, Walker White

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

72 Scopus citations

Abstract

Constraint-based mining of itemsets for questions such as "find all frequent itemsets where the total price is at least $50" has received much attention recently. Two classes of constraints, monotone and antimonotone, have been identified as very useful. There are algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present the first algorithm (called DualMiner) that uses both monotone and antimonotone constraints to prune its search space. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsD. Hand, D. Keim, R. Ng
Pages42-51
Number of pages10
StatePublished - 2002
EventKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Edmonton, Alta, Canada
Duration: Jul 23 2002Jul 26 2002

Other

OtherKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryCanada
CityEdmonton, Alta
Period7/23/027/26/02

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

Cite this

Bucila, C., Gehrke, J., Kifer, D., & White, W. (2002). DualMiner: A dual-pruning algorithm for itemsets with constraints. In D. Hand, D. Keim, & R. Ng (Eds.), Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 42-51)