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Knowledge Discovery using Cartesian Granule Features with Applications

Jimi Shanahan, James Baldwin, Trevor Martin
Current approaches to knowledge discovery can be differentiated based on the discovered models using the following criteria: effectiveness, understandability (to a user or expert in the domain) and evolvability (ability to adapt over time to a changing environment). Most current approaches satisfy understandability or effectiveness, but not simultaneously, while tending to ignore knowledge evolution. Here we show how knowledge representation based upon Cartesian granule features and a corresponding induction algorithm can effectively address these knowledge discovery criteria (in this paper the discussion is limited to understandability and effectiveness) across a wide variety of problem domains including control, image understanding and medical diagnosis.
The proceedings of the Intn'l conference of the North American Fuzzy Information Processing Society, NAFIPS 1999, New York, pp 228-232


NAFIPS99_KD.pdf (79.56 kB)