Abstract

Association rules are statements of the form "for 90 % of the rows of the relation, if the row has value 1 in the columns in set X, then it has 1 also in the columns in set Y ". Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that the rules cannot be presented to the user. We show how the set of rules can be pruned by forming rule covers. A rule cover is a subset of the original set of rules such that for each row in the relation there is an applicable rule in the cover if and only if there is an applicable rule in the original set. We also discuss grouping of association rules by clustering, and present some experimental results of both pruning and grouping. Keywords: data mining, association rules, covers, clustering. 1 Introduction Association rules are an interesting class of database regularities, introduced by Agrawal, Imielinski, and Swami [AIS93]. An association rule is an expres...

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