On computing condensed frequent pattern bases

Jian Pei, Guozhu Dong, Wei Zou, Jiawei Han

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


Frequent pattern mining has been studied extensively. However, the effectiveness and efficiency of this mining is often limited, since the number of frequent patterns generated is often too large. In many applications it is sufficient to generate and examine only frequent patterns with support frequency in close-enough approximation instead of in full precision. Such a compact but close-enough frequent pattern base is called a condensed frequent patterns-base. In this paper, we propose and examine several alternatives at the design, representation, and implementation of such condensed frequent pattern-bases. A few algorithms for computing such pattern-bases are proposed. Their effectiveness at pattern compression and their efficient computation methods are investigated. A systematic performance study is conducted on different kinds of databases, which demonstrates the effectiveness and efficiency of our approach at handling frequent pattern mining in large databases.

Original languageEnglish (US)
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Number of pages8
StatePublished - 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: Dec 9 2002Dec 12 2002

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other2nd IEEE International Conference on Data Mining, ICDM '02

ASJC Scopus subject areas

  • General Engineering


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