Algorithms for clustering data
Algorithms for clustering data
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
A similarity-based probability model for latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Latent semantic indexing: a probabilistic analysis
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Self organization of a massive document collection
IEEE Transactions on Neural Networks
A hybrid system for concept-based web usage mining
International Journal of Hybrid Intelligent Systems
Semantic Web Usage Mining by a Concept-Based Approach for Off-line Web Site Enhancements
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Expert Systems with Applications: An International Journal
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
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The Self Organizing Map (SOM) algorithm has been utilized, with much success, in a variety of applications for the automatic organization of full-text document collections. A great advantage of the SOM method is that document collections can be ordered in such a way so that documents with similar content are positioned at nearby locations of the 2-dimensional SOM lattice. The resulting ordered map thus presents a general view of the document collection which helps the exploration of information contained in the whole document space. The most notable example of such an application is the WEBSOM method where the document collection is ordered onto a map by utilizing word category histograms for representing the documents data vectors. In this paper, we introduce the LSISOM method which resembles WEBSOM in the sense that the document maps are generated from word category histograms rather than simple histograms of the words. However, a major difference between the two methods is that in WEBSOM the word category histograms are formed using statistical information of short word contexts whereas in LSISOM these histograms are obtained from the SOM clustering of the Latent Semantic Indexing representation of document terms.