Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Algebraic aspects of attribute dependencies in information systems
Fundamenta Informaticae
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Fast discovery of association rules
Advances in knowledge discovery and data mining
Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A New Version of Rough Set Exploration System
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Rough Set Model for Constraint-based Multi-dimensional Association Rule Mining
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
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Because the target databases of conventional multidimensional association rule algorithms have no distinction in the role of attributes, a lot of rules can be found and the computing time can be enormous. So, some efficient way is needed to find multidimensioanl associaton rules for a specific class for target database table that has a decision attribute and many conditional attributes. In order to overcome the problem of intensive computig time and possibily generating a lot of unintersting rules, a preprocessing technique that can narrow down the search space is suggested. The method can generate smaller table for multidimensioanl association rule so that computing time can be saved and smaller number of rules are generated. Experiments with a real world data set showed a very good result.