Dynamic knowledge validation and verification for CBR teledermatology system
Artificial Intelligence in Medicine
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Using importance flooding to identify interesting networks of criminal activity
Journal of the American Society for Information Science and Technology
Conceptual distance for association rules post-processing
MEDI'11 Proceedings of the First international conference on Model and data engineering
Finding interesting rules exploiting rough memberships
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Using importance flooding to identify interesting networks of criminal activity
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
Association rule variation with respect to time
Proceedings of the CUBE International Information Technology Conference
Adaptive Study Design Through Semantic Association Rule Analysis
International Journal of Software Science and Computational Intelligence
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Subjective interestingness is at the heart of thesuccessful discovery of association rules. To determine what is subjectively interesting, users' domainknowledge must be applied. [7] introduced an approach that requires very little domain knowledgeand inter action to eliminate the majority of therules that are subjectively not interesting. In thispaper we investigate how this approach can be incorporated into the mining process, the benefits anddisadvantages of doing so, and examine the resultsof its application to real databases.