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...

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava
• Keywords: Association Rule Mining, Data Mining, Desirable Measure, KDD 2002, Keywords Interestingness Measure