Basic Association Rules

Previous approaches for mining association rules generate large sets of association rules. Such sets are difficult for users to understand and manage. Here, the concept of a restricted conditional probability distribution is used to explain an association rule. Based on this concept, a new type of association rules, called basic association rules, is defined. We propose the GenBR algorithm to generate the set of classes of basic association rules. Theoretical analysis shows that the search space of the algorithm can be translated to an ncube graph. The set of classes of basic association rules generated by GenBR is easy for users to understand and manage. Our experiments on synthetic and real datasets show that GenBR is either faster than previous approaches or generates fewer rules or both.

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