Modeling Competence for Case Based Reasoning Systems Using Clustering

The success of the Case Based Reasoning (CBR) system depends on the quality of the case data. This quality is dedicated to the study of the case base competence which is measured by the range of problems that can be satisfactorily solved. In fact, modeling case-base competence is a clamorous issue in the discipline of CBR. However, the existence of erroneous cases as noises and the non uniform problem distributions has not been considered in the proposed computing competence. In this paper, we propose a novel case base competence model based on Mahalanobis distance and a clustering technique named DBSCAN-GM. The advantage of this newly proposed model is its high accuracy for predicting competence. In addition, it is not sensitive to noisy cases and it takes account the situation of the distributed case-base. Withal, we contest that this model has a conspicuous role to play in future CBR research in fields such as the development of new policies for maintaining the case base.