From Associations to Inferences


Choh Man Teng 

Institute for Human and Machine Cognition


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. We developed an alternative formulation called interval association rules which is more appropriate for the task of inference. We will present some theoretical and experimental analyses demonstrating the differences between the two formulations, and show how the interval rules can be applied to make well justified inferences.