Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Analyzing IT business values - A Dominance based Rough Sets Approach perspective
Expert Systems with Applications: An International Journal
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Rules mined from a data set represent knowledge patterns relating premises and decisions in 'if ..., then ...' statements. Premise is a conjunction of elementary conditions relative to independent variables and decision is a conclusion relative to dependent variables. Given a set of rules, it is interesting to rank them with respect to some attractiveness measures. In this paper, we are considering rule attractiveness measures related to three semantics: knowledge representation, prediction and efficiency of intervention based on a rule. Analysis of existing measures leads us to a conclusion that the best suited measures for the above semantics are: support and certainty, a Bayesian confirmation measure, and two measures related to efficiency of intervention, respectively. These five measures induce a partial order in the set of rules. For building a strategy of intervention, we propose rules discovered using the Dominance-based Rough Set Approach – the “at least” type rules indicate opportunities for improving assignment of objects, and the “at most” type rules indicate threats for deteriorating assignment of objects.