Intelligent mining multi dimensional association rules from large inconsistent databases.

Abstract

The widespread use of computer applications, database technologies and data collection techniques have resulted in the accumulation of large amounts of data in databases. This has generated an urgent need for new techniques that can intelligently and automatically transform the processed data into useful information and knowledge. In this paper, we propose an intelligent method for mining multi dimensional association rules from large inconsistent databases. It is called Intelligent Mining Association Rules (IMAR). The proposed IMAR was experimented and studied using three domain data sets. It includes Australian Credit Card (ACC), Jakarta Stock Exchange (JSX), and Cleveland Heart Diseases (CLEV) data sets. The results of this study show that IMAR is a promising method for mining multi dimensional association rules from large inconsistent databases intelligently and accurately, and IMAR is a promising method for solving complex data mining problems.