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
Websom for Textual Data Mining
Artificial Intelligence Review - Special issue on data mining on the Internet
A vector space model for automatic indexing
Communications of the ACM
A comparative study for domain ontology guided feature extraction
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Letter: GraySOFM network for solving classification problems
Neurocomputing
Document clustering for mediated information access
IRSG'99 Proceedings of the 21st Annual BCS-IRSG conference on Information Retrieval Research
Web Page Clustering Using a Fuzzy Logic Based Representation and Self-Organizing Maps
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Multivariate Student-t self-organizing maps
Neural Networks
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Applied Soft Computing
Recognition of word collocation habits using frequency rank ratio and inter-term intimacy
Expert Systems with Applications: An International Journal
Probability-based text clustering algorithm by alternately repeating two operations
Journal of Information Science
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In the novel conceptional self-organizing map model (ConSOM) proposed for text clustering in this paper, neurons and documents can be represented by two vectors: one in extended concept space, and the other in traditional feature space, and weight modification of neuron vector is guided by combination of similarities in both traditional and extended spaces. Experimental results show that by utilizing concept relevance knowledge effectively, ConSOM performs better than traditional ''SOM plus VSM'' mode in text clustering due to its semantic sensitivity.