Association mining in large databases: A re-examination of its measures

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

Abstract

In the literature of data mining and statistics, numerous interestingness measures have been proposed to disclose succinct object relationships of association patterns. However, it is still not clear when a measure is truly effective in large data sets. Recent studies have identified a critical property, null-(transaction) invariance, for measuring event associations in large data sets, but many existing measures do not have this property. We thus re-examine the null-invariant measures and find interestingly that they can be expressed as a generalized mathematical mean, and there exists a total ordering of them. This ordering provides insights into the underlying philosophy of the measures and helps us understand and select the proper measure for different applications.

Original languageEnglish (US)
Title of host publicationKnowledge Discovery in Database
Subtitle of host publicationPKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
PublisherSpringer-Verlag Berlin Heidelberg
Pages621-628
Number of pages8
ISBN (Print)9783540749752
DOIs
StatePublished - 2007
Event11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007 - Warsaw, Poland
Duration: Sep 17 2007Sep 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4702 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007
CountryPoland
CityWarsaw
Period9/17/079/21/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Wu, T., Chen, Y., & Han, J. (2007). Association mining in large databases: A re-examination of its measures. In Knowledge Discovery in Database: PKDD 2007 - 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 621-628). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4702 LNAI). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74976-9_66