Measures for comparing association rule sets

Authors:
Damian Dudek
Affiliations:
Department of Software Development and Internet Technologies, The University of Information Technology and Management "Copernicus", Wroclaw, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

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Abstract

Most experimental methods for evaluating algorithms of association rule mining are based solely on quantitative measures such as correlation between minimum support, number of rules or frequent item-sets and data processing time. In this paper we present new measures for comparing association rule sets. We show that observing rule overlapping, support and confidence in two compared rule sets helps evaluate algorithm quality or measure uniformity of source datasets.