GenMax: An efficient algorithm for mining maximal frequent itemsets

Karam Gouda, Mohammed J. Zaki

Research output: Contribution to journalArticle

127 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 maximally 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
Pages (from-to)223-242
Number of pages20
JournalData Mining and Knowledge Discovery
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Nov 2005
Externally publishedYes

Fingerprint

Frequent Itemsets
Mining
Efficient Algorithms
Search Space
Propagation
Optimization

Keywords

  • Association rules
  • Backtracking search
  • Data mining
  • Frequent itemsets
  • Maximal itemsets

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Information Systems
  • Computer Science (miscellaneous)
  • Mathematics (miscellaneous)

Cite this

GenMax : An efficient algorithm for mining maximal frequent itemsets. / Gouda, Karam; Zaki, Mohammed J.

In: Data Mining and Knowledge Discovery, Vol. 11, No. 3, 01.11.2005, p. 223-242.

Research output: Contribution to journalArticle

Gouda, Karam ; Zaki, Mohammed J. / GenMax : An efficient algorithm for mining maximal frequent itemsets. In: Data Mining and Knowledge Discovery. 2005 ; Vol. 11, No. 3. pp. 223-242.
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