Abstract—Association rules is a popular and well researched method for discovering interesting relation between variables in large databases and association rules, is one of the most important tasks in data mining. The generated strong association rules is depend on the association rule extraction by any algorithm, for example Apriory algorithm or Fp growth etc and the evolution of the rules by interestingness measure, for example support and confidence, lift or interest, correlation coefficient, statistical correlation, leverage, conviction etc. The association rules mining are dependent on both steps equally. The classical model of association rules mining is support-confidence, the interestingness measure of which is the confidence measure. The classical interestingness measure in Association Rules have existed some disadvantage. This paper present measurements that are support and confidence, interest or lift, chi-square test for independency, correlation coefficient, statistical correlation to calculate the strength of association rules. There are other interestingness measures, besides support and confidence which include generality, reliability, peculiarity, novelty, surprising ness, utility, and applicability. This paper investigates the evolution association rule mining.
Index Terms—Association rules, support /confidence, interest/lift, chi-square test for independency, correlation coefficient, statistical correlation.
Varshali Jaiswal is with RKDF, India (e-mail: varshalijaswal@gmail.com)
Cite: Varshali Jaiswal and Jitendra Agarwal, "The Evolution of the Association Rules," International Journal of Modeling and Optimization vol. 2, no. 6, pp. 726-729, 2012.
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