Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
Improved Comprehensibility and Reliability of Explanations via Restricted Halfspace Discretization
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
The Needles-in-Haystack Problem
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
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The application of data mining techniques upon medical data is certainly beneficial forresearchers interested in discerning the com lexity of healthcare rocesses in real-lifeoperational situations.In this paper we resent a methodology,together with its computationalimplementation,for the automated extraction of data-defining CNF symbolic rules frommedical data-sets comprising both annotated and un-annotated attributes.We ropose ahybrid approach for symbolic rule extraction which features a sequence of methods includingdata clustering,data discretization and eventually symbolic rule discovery via rough setapproximation.We present a generic data mining workbench that can generate cluster/class-defining symbolic rules from medical data,such that the resultant symbolic rules are directlya licable to medical rule-based expert systems.