Abstract
AbstractThe role of data mining is to search “the space of candidate hypotheses” to offer solutions, whereas the role of statistics is to validate the hypotheses offered by the data‐mining process. In this paper we propose Association Rules Networks (ARNs) as a structure for synthesizing, pruning, and analyzing a collection of association rules to construct candidate hypotheses. From a knowledge discovery perspective, ARNs allow for a goal‐centric, context‐driven analysis of the output of association rules algorithms. From a mathematical perspective, ARNs are instances of backward‐directed hypergraphs. Using two extensive case studies, we show how ARNs and statistical theory can be combined to generate and test hypotheses. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 1: 260‐279, 2009
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