| dc.description.abstract | In this paper, we reuse the Case-Based Reasoning model presented in our last work (Verma et al., 2018) to create a new knowledge intensive similarity-based clustering method that clusters a case base such that the intra-cluster similarity is maximized. In some domains such as recommender systems, the most similar case may not always be the desired one as a user would like to find the closest, yet significantly different cases. To increase the variety of returned cases, clustering a case base first, before the retrieval is executed increases the diversity of solutions. In this work we demonstrate a methodology to optimize the cluster coherence as well to determine the optimal number of clusters for a given case base. Finally, we present an evaluation of our clustering approach by comparing the results of the quality of clusters obtained using our knowledge intensive similarity-based clustering approach against that of the state-of-the-art K-Means clustering method. | en_US |
