Unsupervised case classification using Kohonen “self-organizing feature map” in a case-based reasoning system

Case based reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. Major areas where CBR is used are: diagnosis, help desk, assessment, design and decision support. CBR solves new problems by adapting previously successful solutions to similar problems. The paper presents a technique that uses Kohonen “self-organizing feature map” (SOM) in improving and enhancing the indexing and retrieving method of cases in a CBR system by clustering cases with similar properties together. The SOM approach has proven to be an efficient method for clustering large data collections, and simultaneously presenting the user with a particular planar representation of the clusters. By using SOM, the system could learn about the emergence of any indices that had not previously been thought significant and thus, increasing the flexibility of the system when it comes to a different indexing scheme. It could also help cases to be retrieved quickly. The nodes in the network converge to form clusters to represent groups of entities with similar properties