A model for association rules based on clustering

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

General Terms:
Algorithms, Theory

Keywords:
association rules, bound, clustering, model, support