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One of the most well-studied problems in data mining is computing association rules from large transactional databases. Often, the rule collections extracted from existing data-mining methods can be far too large to be carefully examined and understood by the data analysts. This paper addresses exactly this issue of overwhelmingly large rule collections by introducing and studying the following problem: Given a large collection R of association rules one wants to pick a subset of them S ? R that best represents the original collection R as well as the dataset from which R was extracted. The paper first quantify the notion of the goodness of a ruleset using two very simple and intuitive definitions.
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