BLOSOM: A framework for mining arbitrary boolean expressions

Lizhuang Zhao, Mohammed J. Zaki, Naren Ramakrishnan

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

26 Citations (Scopus)

Abstract

We introduce a novel framework, called BLOSOM, for mining (frequent) boolean expressions over binary-valued datasets. We organize the space of boolean expressions into four categories: pure conjunctions, pure disjunctions, conjunction of disjunctions, and disjunction of conjunctions. We focus on mining the simplest expressions (the minimal generators) for each class. We also propose a closure operator for each class that yields closed boolean expressions. BLOSOM efficiently mines frequent boolean expressions by utilizing a number of methodical pruning techniques. Experiments showcase the behavior of BLOSOM, and an application study on a real dataset is also given.

Original languageEnglish
Title of host publicationKDD 2006
Subtitle of host publicationProceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages827-832
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

  • Boolean Expression
  • Closed Itemsets
  • Data Mining
  • Minimal Generator

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Zhao, L., Zaki, M. J., & Ramakrishnan, N. (2006). BLOSOM: A framework for mining arbitrary boolean expressions. In KDD 2006: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 827-832). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2006).