Title: Learning best K analogies from data distribution for case-based software effort estimation,
Abstract: Case-Based Reasoning (CBR) has been widely used to generate good software
effort estimates. The predictive performance of CBR is a dataset dependent and
subject to extremely large space of configuration possibilities. Regardless of
the type of adaptation technique, deciding on the optimal number of similar
cases to be used before applying CBR is a key challenge. In this paper we
propose a new technique based on Bisecting k-medoids clustering algorithm to
better understanding the structure of a dataset and discovering the the optimal
cases for each individual project by excluding irrelevant cases. Results
obtained showed that understanding of the data characteristic prior prediction
stage can help in automatically finding the best number of cases for each test
project. Performance figures of the proposed estimation method are better than
those of other regular K-based CBR methods.