Learning from Dissociations^*

Abstract

Standard association rules encapsulate the positive relationship between two sets of items: the presence of X is a good predictor for the simultaneous presence of Y. We argue that the absence of an association rule conveys valuable information as well. Dissociation rules are rules that capture the negative relationship between two sets of items: the presence of X and z is not a good predictor for the presence of Y. We developed a representation for augmenting standard association rules with dissociation information, and presented some experimental results suggesting that such augmented rules can improve the quality of the associations obtained, both in terms of rule accuracy and in terms of using these rules as a guide to making decisions.

This work was supported by NASA NCC2-1239.