Association rules network: Definition and applications

Gaurav Pandey, Sanjay Chawla, Simon Poon, Bavani Arunasalam, Joseph G. Davis

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)260-279
Number of pages20
JournalStatistical Analysis and Data Mining
Volume1
Issue number4
DOIs
StatePublished - Mar 2009
Externally publishedYes

Keywords

  • Association rules
  • Association rules network
  • Data mining

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