WCOID-DG: An approach for case base maintenance based on Weighting, Clustering, Outliers, Internal Detection and Dbsan-Gmeans

•We propose a novel maintenance policy for case based reasoning system (WCOID-DG).•Our aim: a large case base is transformed to a small one with improving its quality.•We use feature weights, outliers detection methods and a new clustering technique DG.•WCOID-GM is able to reduce both the storage requirements and search time.•WCOID-GM is efficient in terms of getting satisfying classification accuracy.

The success of the Case Based Reasoning system depends on the quality of the case data and the speed of the retrieval process that can be costly in time, especially when the number of cases gets bulky. To guarantee the systemʼs quality, maintaining the contents of a case base (CB) becomes unavoidably. In this paper, we propose a novel case base maintenance policy named WCOID-DG: Weighting, Clustering, Outliers and Internal cases Detection based on Dbscan and Gaussian means. Our WCOID-DG policy uses in addition to feature weights and outliers detection methods, a new efficient clustering technique, named DBSCAN-GM (DG) which is a combination of DBSCAN and Gaussian-Means algorithms. The purpose of our WCOID-GM is to reduce both the storage requirements and search time and to focus on balancing case retrieval efficiency and competence for a CB. WCOID-GM is mainly based on the idea that a large CB with weighted features is transformed to a small CB with improving its quality. We support our approach with empirical evaluation using different benchmark data sets to show its competence in terms of shrinking the size of the CB and the research time, as well as, getting satisfying classification accuracy.