17 August 1994 Kohonen self-organizing feature map and its use in clustering
Author Affiliations +
Proceedings Volume 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision; (1994); doi: 10.1117/12.182900
Event: Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, 1994, Munich, Germany
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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Markus Torma, "Kohonen self-organizing feature map and its use in clustering", Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); doi: 10.1117/12.182900; https://doi.org/10.1117/12.182900
PROCEEDINGS
6 PAGES


SHARE
KEYWORDS
Neural networks

Quantization

Artificial neural networks

Pattern recognition

Chemical elements

Signal processing

Biological neural networks

RELATED CONTENT


Back to Top