Case-deletion strategy for maintaining the Case Based Reasoning system

Case-Based Reasoning (CBR) is considered a fundamental modality in Computational Intelligence; It has the ability to accumulate previous cases records of particular past reasoning experiences and retrieve and adjust them in order to help new problem-solving in related situations. Undesirably, this incremental store process causes the uncontrolled case base growth. This mess distresses the competence and the performance of CBR systems after few runs, so the importance of maintenance research. This paper proposes a novel case deletion strategy based on a competence model using a clustering technique. Its target is to decrease both the storage requirements and search time and to spotlight on balancing case retrieval performance and competence of the case base. Series ofcase base maintenance approach tests are conducted, on which, our proposed case base maintenance approach will be tested and evaluated by competence and performance criteria.