Selecting the right objective measure for association analysis

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434 Scopus citations


Objective measures such as support, confidence, interest factor, correlation, and entropy are often used to evaluate the interestingness of association patterns. However, in many situations, these measures may provide conflicting information about the interestingness of a pattern. Data mining practitioners also tend to apply an objective measure without realizing that there may be better alternatives available for their application. In this paper, we describe several key properties one should examine in order to select the right measure for a given application. A comparative study of these properties is made using twenty-one measures that were originally developed in diverse fields such as statistics, social science, machine learning, and data mining. We show that depending on its properties, each measure is useful for some application, but not for others. We also demonstrate two scenarios in which many existing measures become consistent with each other, namely, when support-based pruning and a technique known as table standardization are applied. Finally, we present an algorithm for selecting a small set of patterns such that domain experts can find a measure that best fits their requirements by ranking this small set of patterns.

Original languageEnglish (US)
Pages (from-to)293-313
Number of pages21
JournalInformation Systems
Issue number4
StatePublished - Jun 2004

Bibliographical note

Funding Information:
Recommended by Nick Koudas. This work was partially supported by NSF grant number ACI-9982274, DOE contract number DOE/LLNL W-7045-ENG-48 and by the Army High Performance Computing Research Center contract number DAAD19-01-2-0014. The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by AHPCRC and the Minnesota Supercomputing Institute.


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