From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
On Incorporating Subjective Interestingness Into the Mining Process
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On Characterization and Discovery of Minimal Unexpected Patterns in Rule Discovery
IEEE Transactions on Knowledge and Data Engineering
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Data-mining methods have the drawbacks to generate a very large number of rules, sometimes obvious, useless or not very interesting to the user. In this paper we propose a new approach to find unexpected rules from a set of discovered association rules. This technique is characterized by analyzing the discovered association rules using the user's existing knowledge about the domain represented by a fuzzy domain ontology and then ranking the discovered rules according to the conceptual distance of the rule.