Validation-Based Normalization and Selection of Interestingness Measures for Association Rules

We investigate the problem of tuning and selecting among interestingness measures for association rules. We first derive a parametric normalization factor for such measures that addresses imbalanced itemset sizes, and show how it can be generalized across many previously derived measures. Next, we develop a validation- based framework for both the normalization and selection tasks, based upon mutual information measures over attributes. We then apply this framework to market basket data and user profile data in weblogs, to automatically choose among or fine-tune alternative measures for generating and ranking rules. Finally, we show how the derived normalization factor can significantly improve the sensitivity of interestingness measures when used for pure association rule mining and also for a classification task. We also consider how this data-driven approach can be used for fusion of association rule sets: either those elicited from subject matter experts, or those found using prior background knowledge.