Finding interesting rules from large sets of discovered association rules
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In this paper we examine association rules and their interestingness. Usually these rules are discussed in the world of basket analysis. Instead of customer data we now study the situation with data records of a more general but fixed nature, incorporating quantitative (nonboolean) data. We propose a method for finding interesting rules with the help of fuzzy techniques and taxonomies for the items/attributes. Experiments show that the use of the proposed interestingness measure substantially decreases the number of rules.