ECTD: Evidential Clustering and Case Types Detection for Case Base Maintenance

The key factor for the success of Case Based Reasoning (CBR) systems is the quality of their case bases as well as the time spent in case retrieval process which is mainly depending on case bases' size. Indeed, the speed of the retrieval process is seriously decreasing when the case base becomes so heavy. To vouch for case bases' quality, a maintenance process must be provided. Hence, a field for Case Base Maintenance (CBM) emerges. However, a lot of works in CBM field suffers from some limitations and they generally reduce case base's competence during maintenance, especially when cases involving imprecise or uncertain information. To deal with these problems, we propose, in this paper, a new CBM approach named ECTD, Evidential Clustering and case Types Detection for case base maintenance, which is able to manage imperfection in cases by using belief function theory. The key idea of ECTD approach is to use machine learning technique, more accurately the evidential c-means (ECM). Then, it divides cases relative to the different partitions of clusters into four types so that we can subsequently perform the case base maintenance.