The standard formulation of association rules is suitable for describing patterns found in a given data set, A number of difficulties arise when the standard rules are used to infer about novel instances not included in the original data. In previous work we proposed an alternative formulation called interval association rules which is more appropriate for the task of inference, and developed algorithms and pruning strategies for generating interval rules. In this paper we present some theoretical and experimental analyses demonstrating the differences between the two formulations, and show how each of the two approaches can be beneficial under different circumstances.
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