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Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.

Improving Real-Time Efficiency of Case-Based Reasoning for Medical Diagnosis