AIRCC's International Journal of Computer Science and Information Technology

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Association rules is one of data mining methods for discovering knowledge from large amounts of data in databases. In this paper, we propose an intelligent method for discovering association rules, called IMAR. IMAR is designed through three main phases, i.e., preprocessing, processing and post processing. It has been experimented using three domain data sets, i.e., Australian Credit Card (ACC), Jakarta Stock Exchange (JSX), and Cleveland Heart Diseases (CLEV) data sets. Our experimental results show that IMAR can (i) discover association rules from large inconsistent databases intelligently and accurately, and (ii) reduce the number of generated interesting association rules without losing information and with higher accuracy.

Keywords

Database, Data Mining, Association Rules, Knowledge, Information.
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