document: Selecting the right interestingness measure for association patterns 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 .
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