Abstract

Association rule mining is considered as one of the important tasks of data mining intended towards decision making process. It has been mainly developed to identify interesting associations and/or correlation relationships between frequent itemsets in datasets. A multi-objective genetic algorithm approach is proposed in this paper for the discovery of interesting association rules with multiple criteria i. e. support, confidence and simplicity (comprehensibility). With Genetic Algorithm (GA), a global search can be achieved and system automation is developed, because the proposed algorithm could identify interesting association rules from a dataset without having the user-specified thresholds of minimum support and minimum confidence. The experimental results on various types of datasets show the usefulness and effectiveness of the proposed algorithm.

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