Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Potter's Wheel: An Interactive Data Cleaning System
Proceedings of the 27th International Conference on Very Large Data Bases
To identify suspicious activity in anomaly detection based on soft computing
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
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With the wide applications of computers, database technologies and automated data collection techniques, large amount of data have been continuously collected into databases. It creates great demands for analyzing such data and turning them into useful knowledge. Therefore, it is necessary and interesting to examine how to extract hidden information or knowledge from large amounts of data automatically and intelligently. In this paper, we propose an MML-AR (Mining Multiple Level Association Rules), which integrates rough set and association rule methods. MML-AR model has been implemented and tested using Jakarta Stock Exchange (JSX) databases. Our study concludes that MML-AR model can improve the performance ability of generated interesting rules.