Knowledge discovery in databases: an overview
AI Magazine
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Report on the SIGKDD-2002 panel the perfect data mining tool: interactive or automated?
ACM SIGKDD Explorations Newsletter
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A comparison of feature selection methods for an evolving RSS feed corpus
Information Processing and Management: an International Journal - Special issue: Informetrics
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Domain ontology driven data mining: a medical case study
Proceedings of the 2007 international workshop on Domain driven data mining
Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent Systems
Evaluation of rule interestingness measures in medical knowledge discovery in databases
Artificial Intelligence in Medicine
Data Mining for Business Applications
Data Mining for Business Applications
Scalable pattern mining with Bayesian networks as background knowledge
Data Mining and Knowledge Discovery
Discovering implicit associations among critical biological entities
International Journal of Data Mining and Bioinformatics
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Domain Driven Data Mining
Ontology-driven hypothesis generation to explain anomalous patient responses to treatment
Knowledge-Based Systems
Introduction to the Domain-Driven Data Mining Special Section
IEEE Transactions on Knowledge and Data Engineering
Knowledge-Based Interactive Postmining of Association Rules Using Ontologies
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Connecting the dots between news articles
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Ontology-Enhanced association mining
EWMF'05/KDO'05 Proceedings of the 2005 joint international conference on Semantics, Web and Mining
Literature-based discovery: Beyond the ABCs
Journal of the American Society for Information Science and Technology
Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Knowledge-Based Systems
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Introduction: An important quality of association rules is novelty. However, evaluating rule novelty is AI-hard and has been a serious challenge for most data mining systems. Objective: In this paper, we introduce functional novelty, a new non-pairwise approach to evaluating rule novelty. A functionally novel rule is interesting as it suggests previously unknown relations between user hypotheses. Methods: We developed a novel domain-driven KDD framework for discovering functionally novel association rules. Association rules were mined from cardiovascular data sets. At post-processing, domain knowledge-compliant rules were discovered by applying semantic-based filtering based on UMLS ontology. Their knowledge compliance scores were computed against medical knowledge in Pubmed literature. A cardiologist explored possible relationships between several pairs of unknown hypotheses. The functional novelty of each rule was computed based on its likelihood to mediate these relationships. Results: Highly interesting rules were successfully discovered. For instance, common rules such as diabetes mellitus@?coronary arteriosclerosis was functionally novel as it mediated a rare association between von Willebrand factor and intracardiac thrombus. Conclusion: The proposed post-mining domain-driven rule evaluation technique and measures proved to be useful for estimating candidate functionally novel rules with the results validated by a cardiologist.