Selecting the right interestingness measure for association patterns

Research output: Contribution to conferencePaper

601 Scopus citations

Abstract

Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestingness of association patterns. However, many such measures provide conflicting information about the interestingness of a pattern, and the best metric to use for a given application domain is rarely known. In this paper, we present an overview of various measures proposed in the statistics, machine learning and data mining literature. We describe several key properties one should examine in order to select the right measure for a given application domain. A comparative study of these properties is made using twenty one of the existing measures. We show that each measure has different properties which make them useful for some application domains, but not for others. We also present two scenarios in which most of the existing measures agree with each other, namely, support-based pruning and table standardization. Finally, we present an algorithm to select a small set of tables such that an expert can select a desirable measure by looking at just this small set of tables.

Original languageEnglish (US)
Pages32-41
Number of pages10
DOIs
StatePublished - Jan 1 2002
EventKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Edmonton, Alta, Canada
Duration: Jul 23 2002Jul 26 2002

Other

OtherKDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryCanada
CityEdmonton, Alta
Period7/23/027/26/02

Keywords

  • Associations
  • Contingency tables
  • Interestingness measure

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    Tan, P. N., Kumar, V., & Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. 32-41. Paper presented at KDD - 2002 Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alta, Canada. https://doi.org/10.1145/775047.775053