The 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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