Improvement of visual stability by adjusting initial map of SOM and using hierarchical clustering

Keywords

self-organizing map, feature maps, visual stability, improvement method, clustering.

Abstract

SOM learning is influenced by the se-quence of learning data and the initial feature map. In conventional method, ini-tial value of feature map has set at ran-dom, so a different mapping appears even by same input data, so different impres-sions could be increased to the same data in different diagnosis. In this paper, we focused on visual stability of SOM fea-ture map, and we proposed new initializa-tion methods of SOM feature map and new map clustering method using hierar-chical clustering. By experiments, pro-posed methods are visually stable than conventional method in the view of node mapping.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Tsutomu Miyoshi
AU  - Shinji Momoi
PY  - 2014/01
DA  - 2014/01
TI  - Improvement of visual stability by adjusting initial map of SOM and using hierarchical clustering
BT  - Proceedings of the 2013 International Conference on Advances in Intelligent Systems in Bioinformatics
PB  - Atlantis Press
SP  - 1
EP  - 6
SN  - 1951-6851
UR  - https://www.atlantis-press.com/article/11349
ID  - Miyoshi2014/01
ER  -