Efficiently mining maximal frequent itemsets

Karam Gouda, Mohammed J. Zaki

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

276 Citations (Scopus)

Abstract

We present GenMax, a backtrack search based algorithm for mining maximal frequent itemsets. GenMax uses a number of optimizations to prune the search space. It uses a novel technique called progressive focusing to perform maximality checking, and diffset propagation to perform fast frequency computation. Systematic experimental comparison with previous work indicates that different methods have varying strengths and weaknesses based on dataset characteristics. We found GenMax to be a highly efficient method to mine the exact set of maximal patterns.

Original languageEnglish
Title of host publicationProceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
Pages163-170
Number of pages8
Publication statusPublished - 1 Dec 2001
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: 29 Nov 20012 Dec 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
CountryUnited States
CitySan Jose, CA
Period29/11/012/12/01

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Gouda, K., & Zaki, M. J. (2001). Efficiently mining maximal frequent itemsets. In Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01 (pp. 163-170). (Proceedings - IEEE International Conference on Data Mining, ICDM).