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 achanging 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 acorresponding 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 understandingand medical diagnosis.
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