Data Mining and Knowledge Discovery

, Volume 28, Issue 4, pp 1004–1045

Behavior-based clustering and analysis of interestingness measures for association rule mining


DOI: 10.1007/s10618-013-0326-x

Cite this article as:
Tew, C., Giraud-Carrier, C., Tanner, K. et al. Data Min Knowl Disc (2014) 28: 1004. doi:10.1007/s10618-013-0326-x


A number of studies, theoretical, empirical, or both, have been conducted to provide insight into the properties and behavior of interestingness measures for association rule mining. While each has value in its own right, most are either limited in scope or, more importantly, ignore the purpose for which interestingness measures are intended, namely the ultimate ranking of discovered association rules. This paper, therefore, focuses on an analysis of the rule-ranking behavior of 61 well-known interestingness measures tested on the rules generated from 110 different datasets. By clustering based on ranking behavior, we highlight, and formally prove, previously unreported equivalences among interestingness measures. We also show that there appear to be distinct clusters of interestingness measures, but that there remain differences among clusters, confirming that domain knowledge is essential to the selection of an appropriate interestingness measure for a particular task and business objective.


Interestingness measuresClusteringBehavior analysisAssociation rule mining

Copyright information

© The Author(s) 2013

Authors and Affiliations

  • C. Tew
    • 1
  • C. Giraud-Carrier
    • 1
  • K. Tanner
    • 1
  • S. Burton
    • 1
  1. 1.Department of Computer ScienceBrigham Young UniversityProvoUSA