Self-organizing maps
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Clustering Algorithms
SOM-Based Methodology for Building Large Text Archives
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Data visualisation and manifold mapping using the ViSOM
Neural Networks - New developments in self-organizing maps
Marginal median SOM for document organization and retrieval
Neural Networks
An analysis of the relative hardness of Reuters-21578 subsets: Research Articles
Journal of the American Society for Information Science and Technology
Adaptive topological tree structure for document organisation and visualisation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Large-scale data exploration with the hierarchically growing hyperbolic SOM
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
On the equivalence between kernel self-organising maps and self-organising mixture density networks
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Hybrid SOM-Based Document Organization System
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Semi-supervised single-label text categorization using centroid-based classifiers
Proceedings of the 2007 ACM symposium on Applied computing
Relevance and kernel self-organising maps
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Modified ART 2A growing network capable of generating a fixed number of nodes
IEEE Transactions on Neural Networks
Multivariate Student-t self-organizing maps
Neural Networks
Recognition of word collocation habits using frequency rank ratio and inter-term intimacy
Expert Systems with Applications: An International Journal
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The large volume of nowadays document collections has increased the need of fast trainable document organization systems. This paper presents and evaluates a hybrid system to self-organization of massive document collections based on self-organizing map (SOM). The hybrid system uses prototypes generated by a clustering algorithm to train the document maps, thus reducing the training time of large maps. We test the system with k-means and modified leader clustering algorithms. The experiments are carried out with the Reuters-21758 v1.0 and 20 Newsgroup collections. The performance of the system is measured in terms of text categorization effectiveness on test set and training time. Experimental results show that the proposed system generates effective document maps in less time than SOM. However, the hybrid system using k-means generates better document maps than the one using modified leader at the cost of more long training time.