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Proceedings Paper

Kohonen self-organizing feature map and its use in clustering
Author(s): Markus Torma
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Paper Abstract

Cluster analysis is an important part of pattern recognition. In this paper we present the applicability of one neural network model, namely Kohonen self-organizing feature map, to cluster analysis. The aim is to develop a method which could determine the correct number of clusters by itself. First, the general concept of neural networks and detailed introduction to Kohonen self-organizing feature map are discussed. Then, the suitability of Kohonen self- organizing feature map to cluster analysis is discussed and some simulations are presented.

Paper Details

Date Published: 17 August 1994
PDF: 6 pages
Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); doi: 10.1117/12.182900
Show Author Affiliations
Markus Torma, Helsinki Univ. of Technology (Finland)


Published in SPIE Proceedings Vol. 2357:
ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision
Heinrich Ebner; Christian Heipke; Konrad Eder, Editor(s)

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