Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Discovery of Surprising Exception Rules Based on Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
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ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Transactions on rough sets XII
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
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JSAI'03/JSAI04 Proceedings of the 2003 and 2004 international conference on New frontiers in artificial intelligence
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PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
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Applied Intelligence
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LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Information-Based pruning for interesting association rule mining in the item response dataset
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
BruteSuppression: a size reduction method for Apriori rule sets
Journal of Intelligent Information Systems
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International Journal of Intelligent Systems Technologies and Applications
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This paper presents a method for mining exception rules based on a novel measure which estimates interestingness relative to its corresponding common sense rule and reference rule. Mining interesting rules is one of the important data mining tasks. Interesting rules bring novel knowledge that helps decision makers for advantageous actions. It is true that interestingness is a relative issue that depends on the other prior knowledge. However, this estimation can be biased due to the incomplete or inaccurate knowledge about the domain. Even if possible to estimate interestingness, it is not so trivial to judge the interestingness from a huge set of mined rules. Therefore, an automated system is required that can exploit the knowledge extractacted from the data in measuring interestingness. Since the extracted knowledge comes from the data, so it is possible to find a measure that is unbiased from the user's own belief. An unbiased measure that can estimate the interestingness of a rule with respect to the extractacted rules can be more acceptable to the user. In this work we try to show through the experiments, how our proposed relative measure can give an unbiased estimate of relative interestingness in a rule considering already mined rules.