Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Communications of the ACM
Knowledge discovery in databases: an attribute-oriented rough set approach
Knowledge discovery in databases: an attribute-oriented rough set approach
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Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Efficient Adaptive-Support Association Rule Mining for Recommender Systems
Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
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TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
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AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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Fundamenta Informaticae - Special issue on the 9th international conference on rough sets, fuzzy sets, data mining and granular computing (RSFDGrC 2003)
Journal of the American Society for Information Science and Technology
A rough set based model to rank the importance of association rules
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Exploiting Rough Argumentation in an Online Dispute Resolution Mediator
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
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Knowledge-Based Systems
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Transactions on rough sets VI
Selection of important attributes for medical diagnosis systems
Transactions on rough sets VII
Rule-Based approach to computational stylistics
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
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Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, interesting and important. We introduce a rough set based Rule Importance Measure to select the most important rules. We use ROSETTA software to generate multiple reducts. Apriori association rule algorithm is then applied to generate rule sets for each data set based on each reduct. Some rules are generated more frequently than the others among the total rule sets. We consider such rules as more important. We define rule importance as the frequency of an association rule generated across all the rule sets. Rule importance is different from either rule interestingness measures or rule quality measures because of their application tasks, the processes where the measures are applied and the contents they measure. The experimental results from an artificial data set, UCI machine learning datasets and an actual geriatric care medical data set show that our method reduces the computational cost for rule generation and provides an effective measure of how important is a rule.