Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Self-Organizing Maps
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Data mining for hypertext: a tutorial survey
ACM SIGKDD Explorations Newsletter
Selforganizing classification on the Reuters news corpus
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Hybrid Neural Document Clustering Using Guided Self-Organization and WordNet
IEEE Intelligent Systems
Binary tree time adaptive self-organizing map
Neurocomputing
Clustering web documents based on knowledge granularity
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
A game-theoretic approach to competitive learning in self-organizing maps
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Categorization of large text collections: feature selection for training neural networks
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A new unsupervised feature selection method for text clustering based on genetic algorithms
Journal of Intelligent Information Systems
A novel self-adaptive clustering algorithm for dynamic data
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Clustering by document concepts is a powerful way ofretrieving information from a large number of documents.This task in general does not make any assumption on thedata distribution. In this paper, for this task we propose anew competitive Self-Organising (SOM) model, namelythe Dynamic Adaptive Self-Organising Hybrid model(DASH). The features of DASH are a dynamic structure,hierarchical clustering, non-stationary data learning andparameter self-adjustment. All features are data-oriented:DASH adjusts its behaviour not only by modifying itsparameters but also by an adaptive structure. Thehierarchical growing architecture is a useful facility forsuch a competitive neural model which is designed fortext clustering. In this paper, we have presented a newtype of self-organising dynamic growing neural networkwhich can deal with the non-uniform data distributionand the non-stationary data sets and represent the innerdata structure by a hierarchical view.