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Self-Organizing Maps of Large Document Collections

  • Timo Honkela
  • Krista Lagus
  • Samuel Kaski
Part of the Springer Finance book series (FINANCE)

Abstract

All applications presented in the previous chapters applied self-organizing maps to reducing quantitative, numeric data. This chapter shows how textual information can be treated in a similar way and how self-organizing maps can help in more effective retrieval of information than current search engines. The use of WEBSOM is a novel method for organizing collections of text documents into maps, for browsing and exploring links on the World Wide Web, or for organization of electronic messages or files. Timo Honkela and the team at the Neural Network Center at HUT provide several examples of the use of WEBSOM and many more are available on their website (http://nodulus.hut.fi/websomn/).

Keywords

Document Collection Text Document Vector Space Model Latent Semantic Indexing Context Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Timo Honkela
  • Krista Lagus
  • Samuel Kaski

There are no affiliations available

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