Abstract. Today's case based reasoning applications face several challenges. In a typical application, the case bases grow at a very fast rate and their contents become increasingly diverse, making it necessary to partition a large case base into several smaller ones. Their users are overloaded with vast amounts of information during the retrieval process. These problems call for the development of effective case-base maintenance methods. As a result, many researchers have been driven to design sophisticated case-base structures or maintenance methods. In contrast, we hold a different point of view: we maintain that the structure of a case base should be kept as simple as possible, and that the maintenance method should be as transparent as possible. In this paper we propose a case-base maintenance method that avoids building sophisticated structures around a case base or perform complex operations on a case base. Our method partitions cases into clusters where the cases in the same cluster are more similar than cases in other clusters. In addition to the content of textual cases, the clustering method we propose can also be based on values of attributes that may be attached to the cases. Clusters can be converted to new case bases, which are smaller in size and when stored distributedly, can entail simpler maintenance operations. The contents of the new case bases are more focused and easier to retrieve and update. To support retrieval in this distributed case-base network, we present a method that is based on a decision forest built with the attributes that are obtained through an innovative modification of the ID3 algorithm.
Finally, a case-base maintenance method that avoids building sophisticated structures around a casebase or complex operations is presented by Yang and Wu REF .
Keep It Simple: A Case-Base Maintenance Policy Based on Clustering and Information Theory
Canadian Conference on AI
Computer Science