Role of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach

Details Role of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach Role of Interestingness Measures in CAR Rule Ordering for Associative Classifier: An Empirical Approach

2010-01-20

arxiv.org/abs/1001.3478v1

Journal of Computing, Vol. 2, Issue 1, January 2010

Computer Science

Learning

Scientific paper

Associative Classifier is a novel technique which is the integration of Association Rule Mining and Classification. The difficult task in building Associative Classifier model is the selection of relevant rules from a large number of class association rules (CARs). A very popular method of ordering rules for selection is based on confidence, support and antecedent size (CSA). Other methods are based on hybrid orderings in which CSA method is combined with other measures. In the present work, we study the effect of using different interestingness measures of Association rules in CAR rule ordering and selection for associative classifier.

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