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Case-Based Reasoning (CBR) has been widely used to generate good softwareeffort estimates. The predictive performance of CBR is a dataset dependent andsubject to extremely large space of configuration possibilities. Regardless ofthe type of adaptation technique, deciding on the optimal number of similarcases to be used before applying CBR is a key challenge. In this paper wepropose a new technique based on Bisecting k-medoids clustering algorithm tobetter understanding the structure of a dataset and discovering the the optimalcases for each individual project by excluding irrelevant cases. Resultsobtained showed that understanding of the data characteristic prior predictionstage can help in automatically finding the best number of cases for each testproject. Performance figures of the proposed estimation method are better thanthose of other regular K-based CBR methods.

Learning best K analogies from data distribution for case-based software effort estimation