Evaluating generalized association rules through objective measures
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Incremental maintenance of generalized association rules under taxonomy evolution
Journal of Information Science
A method for mining quantitative association rules
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
A cost-efficient and versatile sanitizing algorithm by using a greedy approach
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
A hybrid interestingness heuristic approach for attribute-oriented mining
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
A hybrid heuristic approach for attribute-oriented mining
Decision Support Systems
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The goal of the paper is to mine generalizedassociation rules using pruning techniques. Given a largetransaction database and a hierarchical taxonomy tree ofthe items, we try to find the association rules between theitems at different levels in the taxonomy tree under theassumption that original frequent itemsets and associationrules have already been generated beforehand. In theproposed algorithm GMAR, we use join methods andpruning techniques to generate new generalizedassociation rules. Through several comprehensiveexperiments, we find that the GMAR algorithm is muchbetter than BASIC and Cumulate algorithms.