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Interestingness Measures for Association Rules: What Do They Really Measure?
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Author(s): Yun Sing Koh (Auckland University of Technology, New Zealand), Richard O’Keefe (University of Otago, New Zealand)and Nathan Rountree (University of Otago, New Zealand)
Copyright: 2008
Pages: 23
Source title:
Data Mining and Knowledge Discovery Technologies
Source Author(s)/Editor(s): David Taniar (Monash University, Australia)
DOI: 10.4018/978-1-59904-960-1.ch002
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Abstract
Association rules are patterns that offer useful information on dependencies that exist between the sets of items. Current association rule mining techniques such as apriori often extract a very large number of rules. To make sense of these rules we need to order or group the rules in some fashion such that the useful patterns are highlighted. The study of this process involves the investigation of an “interestingness” in the rules. To date, various measures have been proposed but unfortunately, these measures present inconsistent information about the interestingness of a rule. In this chapter, we show that different metrics try to capture different dependencies among variables. Each measure has its own selection bias that justifies the rationale for preferring it compared to other measures. We present an experimental study of the behaviour of the interestingness measures such as lift, rule interest, Laplace, and information gain. Our experimental results verify that many of these measures are very similar in nature. From the findings, we introduce a classification of the current interestingness measures.
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