Clustering is employed in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are jointly relevant to some topics are assigned to the same cluster. This relevance can be determined by examining the index term representation of documents or by capturing user feedback on queries to the system. In cluster-oriented systems, the retrieval process can be enhanced by employing characterization of clusters. In this paper, we present the techniques to develop clusters and cluster characterizations by employing user viewpoint. The user viewpoint is elicited through a structured interview based on a knowledge acquisition technique, namely Personal Construct Theory. It is demonstrated that the application of personal construct theory results in an adequate representation of clusters that can be used during query as well as to assign new documents to the appropriate clusters.