Comparison of hierarchic agglomerative clustering methods for document retrieval
The Computer Journal
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Concept decompositions for large sparse text data using clustering
Machine Learning
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Web Communities Based on the Co-Occurence of References
DS '00 Proceedings of the Third International Conference on Discovery Science
A method of cluster-based indexing of textual data
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A co-evolutionary framework for clustering in information retrieval systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A method of cluster-based indexing of textual data
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Using visual linkages for multilingual image retrieval
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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In this paper, we present an approach to clustering in text-based information retrieval systems. The proposed method generates overlapping clusters, each of which is composed of subsets of associated terms and documents with normalized significance weights. In the paper, we first briefly introduce the probabilistic formulation of our clustering scheme and then show the procedure for cluster generation. We also report some experimental results, where the generated clusters are investigated in the framework of automatic text categorization.