Extraction of Interesting Association Rules Using Genetic Algorithms

The process of discovering interesting and unexpected rules from large data sets is known as association rule mining. The typical approach is to make strong simplifying assumptions about the form of the rules, and limit the measure of rule quality to simple properties such as support or confidence. Support and confidence limit the level of interestingness of the generated rules. Comprehensibility, J-Measure and predictive accuracy are metrics that can be used together to find interesting association rules. Because these measures have to be used differently as measures of the quality of the rule, they can be considered as different objectives of the association rule mining problem. The association rule mining problem, therefore, can be modelled as a multi-objective problem rather than as a single-objective problem. In this paper we present a Pareto-based multiobjective evolutionary algorithm rule mining method based on genetic algorithms. Predictive accuracy, comprehensibility and interestingness are used as different objectives of the association rule mining problem. Specific mechanisms for mutations and crossover operators together with elitism have been designed to extract interesting rules from a transaction database. Empirical results of experiments carried out indicate high predictive accuracy of the rules generated..