User-oriented document clustering: a framework for learning in information retrieval
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
Generation and search of clustered files
ACM Transactions on Database Systems (TODS)
Semantic Clustering of Index Terms
Journal of the ACM (JACM)
Cluster characterization in information retrieval
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
A semi-supervised document clustering technique for information organization
Proceedings of the ninth international conference on Information and knowledge management
Combining preference- and content-based approaches for improving document clustering effectiveness
Information Processing and Management: an International Journal
A collaborative filtering-based approach to personalized document clustering
Decision Support Systems
Combining preference- and content-based approaches for improving document clustering effectiveness
Information Processing and Management: an International Journal
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User-oriented clustering schemes enable the classification of documents based upon the user perception of the similarity between documents, rather than on some similarity function presumed by the designer to represent the user criteria. In this paper, an enhancement of such a clustering scheme is presented. This is accomplished by the formulation of the user-oriented clustering as a function-optimization problem. The problem formulated is termed the Boundary Selection Problem (BSP). Heuristic approaches to solve the BSP are proposed and a preliminary for evaluation of these approaches is provided.