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}
}
  • T. Kohonen
  • Published 9 June 1997
  • Computer Science
  • Proceedings of International Conference on Neural Networks (ICNN'97)
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… 

Figures from this paper

Self organization of a massive document collection
TLDR
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
TLDR
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.
Tree view self-organisation of web content
Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997
TLDR
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
TLDR
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
TLDR
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.
Auto-tagging of Text Documents into XML
TLDR
A novel system which automatically converts text documents into XML by extracting information from previously tagged XML documents by using the Self-Organizing Map learning algorithm to arrange tagged documents on a two-dimensional map such that nearby locations contain similar documents.
Mining with the WEBSOM
TLDR
Experiments on document collections of various sizes, text types, and languages show that the WEBSOM method is scalable and generally applicable and preliminary results in a text retrieval experiment indicate that even when the additional value provided by the visualization is disregarded the document maps perform at least comparably with more conventional retrieval methods.
Automating XML Markup using Machine Learning Techniques
TLDR
A novel system for automatically marking up text documents into XML using the techniques of the Self-Organising Map (SOM) algorithm in conjunction with an inductive learning algorithm, C5.0.
Text mining with the WEBSOM
TLDR
Experiments on document collections of various sizes, text types, and languages show that the WEBSOM method is scalable and generally applicable and preliminary results in a text retrieval experiment indicate that even when the additional value provided by the visualization is disregarded the document maps perform at least comparably with more conventional retrieval methods.
...
...

References

SHOWING 1-5 OF 5 REFERENCES
Very Large Two-Level SOM for the Browsing of Newsgroups
TLDR
The main features of this Self-Organizing Maps system, called the WEBSOM, are described, as well as some newer developments of it.
Content-addressable memories
TLDR
This book discusses Associative Memory, Content Addressing, and Associative Recall, and the CAM by the Linear-Select Memory Principle, as well as Logic Principles of Content-Addressable Memories.
Self-Organization and Associative Memory
TLDR
The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Self-Organizing Maps
  • T. Kohonen
  • Computer Science
    Springer Series in Information Sciences
  • 1995
TLDR
The mathematical preliminaries, background, basic ideas, and implications of the Self-Organising Map algorithm are expounded in a manner which is accessible without prior expert knowledge.
Self-Organizing Maps, Series in Information Sciences
  • Self-Organizing Maps, Series in Information Sciences
  • 1995