Dimensionality Reduction of very large document collections by Semantic Mapping

Autor/innen

  • Renato Fernandes Corrêa
  • Teresa Bernarda Ludermir

DOI:

https://doi.org/10.2390/biecoll-wsom2007-129

Schlagworte:

Document Clustering, Dimensionality Reduction, Semantic Mapping, DDC: 004 (Data processing, computer science, computer systems)

Abstract

This paper describes improving in Semantic Mapping, a feature extraction method useful to dimensionality reduction of vectors representing documents of large text collections. This method may be viewed as a specialization of the Random Mapping, method proposed in WEBSOM project. Semantic Mapping, Random Mapping and Principal Component Analysis (PCA) are applied to categorization of document collections using Self-Organizing Maps (SOM). Semantic Mapping generated document representation as good as PCA and much better than Random Mapping.

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Veröffentlicht

2007-12-31