Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Building an Association Rules Framework to Improve Product Assortment Decisions
Data Mining and Knowledge Discovery
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Regression analysis of the number of association rules
International Journal of Automation and Computing
Dynamic rank correlation computing for financial risk analysis
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
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In their paper [1], S. Brin, R. Matwani and C. Silverstien discussed measuring significance of (generalized) association rules via the support and the chi-squared test for correlation. They provided some illustrative examples and pointed that the chi-squared test needs to be agumented by a measure of interest that they also suggested.This paper presents a further elaboration and extension of their discussion. As suggested by Brin et al, the chi-squared test succeeds in measuring the cell dependencies in a 2x2 contingency table. However, it can be misleading in cases of bigger contingency tables. We will give some illustrative examples based on those presented in [1]. We will also propose a more appropriate reliability measure of association rules.