Mining Association Rules with Respect to Support and Anti-support-Experimental Results

Authors:
Roman Słowiński;Izabela Szczęch;Mirosław Urbanowicz;Salvatore Greco
Affiliations:
Inst. of Computing Science, Poznań University of Technology,60-965 Poznań, Poland and Inst. for Systems Research, Polish Academy of Sciences, 01---447 Warsaw, Poland;Inst. of Computing Science, Poznań University of Technology,60-965 Poznań, Poland;Inst. of Computing Science, Poznań University of Technology,60-965 Poznań, Poland;Faculty of Economics, University of Catania, Corso Italia, 55, 95129 Catania, Italy
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

Evaluating the interestingness of rules or trees is a challenging problem of knowledge discovery and data mining. In recent studies, the use of two interestingness measures at the same time was prevailing. Mining of Pareto-optimal borders according to support and confidence, or support and anti-support are examples of that approach. Here, we consider induction of "if..., then..." association rules with a fixed conclusion. We investigate ways to limit the set of rules non---dominated wrt support and confidence or support and anti-support, to a subset of truly interesting rules. Analytically, and through experiments, we show that both of the considered sets can be easily reduced by using the valuable semantics of confirmation measures.