Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
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IntroductionAn 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.
论文关键词：Association rules,Data mining methods,Interactive data exploration and discovery,Medical knowledge support systems,Rule interestingness
论文评审过程：Received 8 September 2010, Revised 16 December 2010, Accepted 22 January 2011, Available online 1 February 2011.
论文官网地址：https://doi.org/10.1016/j.knosys.2011.01.008
