Exploration of very large databases by self-organizing maps
@article{Kohonen1997ExplorationOV,
title={Exploration of very large databases by self-organizing maps},
author={Teuvo Kohonen},
journal={Proceedings of International Conference on Neural Networks (ICNN'97)},
year={1997},
volume={1},
pages={PL1-PL6 vol.1}
}This paper describes a data organization system and genuine content-addressable memory called the WEBSOM. It is a two-layer self-organizing map (SOM) architecture where documents become mapped as points on the upper map, in a geometric order that describes the similarity of their contents. By standard browsing tools one can select from the map subsets of documents that are most similar mutually. It is also possible to submit free-form queries about the wanted documents whereby the WEBSOM…
181 Citations
Self organization of a massive document collection
- Computer ScienceIEEE Trans. Neural Networks Learn. Syst.
- 2000
A system that is able to organize vast document collections according to textual similarities based on the self-organizing map (SOM) algorithm, based on 500-dimensional vectors of stochastic figures obtained as random projections of weighted word histograms.
Self-Organizing Maps of Very Large Document Collections: Justification for the WEBSOM Method
- Computer Science
- 1998
The WEBSOM method is based on using the Self-Organizing Map algorithm for automatically learning relevant structures in the text and for organizing the document collection.
Self-organising maps for tree view based hierarchical document clustering
- Computer ScienceProceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)
- 2002
This paper presents a hierarchical and growing method using a series of 1D maps for clustering documents and browsing them in a dynamically generated tree of topics to demonstrate the efficiency of the method.
Websom for Textual Data Mining
- Computer ScienceArtificial Intelligence Review
- 2004
Different kinds of information needs and tasks concerning organizing, visualizing, searching, categorizing and filtering textual data are considered, and examples how document maps can aid in these situations are presented.
A self-organizing map for concept classification in information retrieval
- Computer ScienceProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
- 2005
A new model based on the self-organizing map paradigm to discover the concepts embedded in a collection of documents, without manual category labelling is proposed and tested on a TREC-6 subcollection.
Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997
- Education
- 1998
A comprehensive list of papers that use the Self-Organizing Map algorithms, have bene ted from them, or contain analyses of them is collected and provided both a thematic and a keyword index to help find articles of interest.
An hybrid architecture for clusters analysis: rough setstheory and self-organizing map artificial neural network
- Computer Science
- 2012
This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis, and results evidence the better performance using the Hybrid Architecture than Self-organizing Map.
FOR CLUSTERS ANALYSIS : ROUGH SETS THEORY AND SELF-ORGANIZING MAP ARTIFICIAL NEURAL NETWORK
- Computer Science
- 2012
This work consists in the use of Rough Sets Theory, in order to pre-processing data to be presented to Self-Organizing Map neural network (Hybrid Architecture) for clusters analysis, and results evidence the better performance using the Hybrid Architecture than Self-organizing Map.
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