Efficiently mining maximal frequent itemsets

Karam Gouda, Mohammed J. Zaki

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

269 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 - IEEE International Conference on Data Mining, ICDM
Pages163-170
Number of pages8
Publication statusPublished - 1 Dec 2001
Externally publishedYes
Event1st IEEE International Conference on Data Mining, ICDM'01 - San Jose, CA, United States
Duration: 29 Nov 20012 Dec 2001

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 - IEEE International Conference on Data Mining, ICDM (pp. 163-170)

Efficiently mining maximal frequent itemsets. / Gouda, Karam; Zaki, Mohammed J.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2001. p. 163-170.

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

Gouda, K & Zaki, MJ 2001, Efficiently mining maximal frequent itemsets. in Proceedings - IEEE International Conference on Data Mining, ICDM. pp. 163-170, 1st IEEE International Conference on Data Mining, ICDM'01, San Jose, CA, United States, 29/11/01.
Gouda K, Zaki MJ. Efficiently mining maximal frequent itemsets. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2001. p. 163-170
Gouda, Karam ; Zaki, Mohammed J. / Efficiently mining maximal frequent itemsets. Proceedings - IEEE International Conference on Data Mining, ICDM. 2001. pp. 163-170
@inproceedings{0e6af78a5b034e08a580096e994cb818,
title = "Efficiently mining maximal frequent itemsets",
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.",
author = "Karam Gouda and Zaki, {Mohammed J.}",
year = "2001",
month = "12",
day = "1",
language = "English",
isbn = "0769511198",
pages = "163--170",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - Efficiently mining maximal frequent itemsets

AU - Gouda, Karam

AU - Zaki, Mohammed J.

PY - 2001/12/1

Y1 - 2001/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78149351437&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149351437&partnerID=8YFLogxK

M3 - Conference contribution

SN - 0769511198

SN - 9780769511191

SP - 163

EP - 170

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -