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Mining Condensed Frequent-Pattern Bases

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Abstract

Frequent-pattern mining has been studied extensively and has many useful applications. However, frequent-pattern mining often generates too many patterns to be truly efficient or effective. In many applications, it is sufficient to generate and examine frequent patterns with a sufficiently good approximation of the support frequency instead of in full precision. Such a compact but “close-enough” frequent-pattern base is called a condensed frequent-pattern base.

In this paper, we propose and examine several alternatives for the design, representation, and implementation of such condensed frequent-pattern bases. Several algorithms for computing such pattern bases are proposed. Their effectiveness at pattern compression and methods for efficiently computing them are investigated. A systematic performance study is conducted on different kinds of databases, and demonstrates the effectiveness and efficiency of our approach in handling frequent-pattern mining in large databases.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, State University of New York at Buffalo, 201 Bell Hall, Buffalo, NY, 14260-2000, USA

    Jian Pei

  2. Wright State University, USA

    Guozhu Dong

  3. University of Illinois at Urbana-Champaign, Urbana-Champaign, USA

    Wei Zou & Jiawei Han

Authors
  1. Jian Pei
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  2. Guozhu Dong
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  3. Wei Zou
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  4. Jiawei Han
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Correspondence to Jian Pei.

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Pei, J., Dong, G., Zou, W. et al. Mining Condensed Frequent-Pattern Bases. Know. Inf. Sys. 6, 570–594 (2004). https://doi.org/10.1007/s10115-003-0133-6

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  • DOI: https://doi.org/10.1007/s10115-003-0133-6

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