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Home List of Titles Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/167108
- Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
- Sebastian, Y.; Then, Patrick H. H.
- 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.
- Publication type
- Journal article
- Research centre
- Swinburne University of Technology. Sarawak Campus. School of Engineering, Computing and Science
- Knowledge-Based Systems, Vol.24, no. 5 (Jul 2011), pp. 609-620
- Publication year
- FOR Code(s)
- 0806 Information Systems; 1503 Business and Management; 1702 Cognitive Sciences
- Association rules; Data mining methods; Databases; Knowledge discovery; Medical knowledge support systems; Rule interestingness
- Publisher URL
- Copyright © 2011 Elsevier B.V. All rights reserved.
- Additional information
- The authors acknowledge support from Malaysia's Ministry of Science, Technology and Innovation (MOSTI) Science Fund 2009 no. 01-02-14-SF0006.
- Peer reviewed