| Résumé: | Association rules, introduced by Agrawal, Imielinski and Swami, is one of data mining technique to discover interesting rules or relationships among attributes in databases. It has attracted great attention in database research communities in recent years. In this paper, we propose a Mining Association Rules (MAR) model which integrate intelligent and data analysis techniques. MAR model has been implemented and tested using Jakarta Stock Exchange (JSA) databases. Our study conclude that MAR model can improve the performance ability of generated rules. In this paper, we explain the proposed MAR model, testing and experimental results in looking into the performance of the model and conclusion.
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