A model for association rules based on clustering

Authors:
Carlos Ordonez
Affiliations:
Teradata, NCR, San Diego, CA
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

Citing 7
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Abstract

Association rules and clustering are fundamental data mining techniques used for different goals. We propose a unifying theory by proving association support and rule confidence can be bounded and estimated from clusters on binary dimensions. Three support metrics are introduced: lower, upper and average support. Three confidence metrics are proposed: lower, upper and average confidence. Clusters represent a simple model that allows understanding and approximating association rules, instead of searching for them in a large transaction data set.