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 (the 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. 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.
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