Proceedings of the 2013 International Conference on Advances in Intelligent Systems in Bioinformatics

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

Authors
Tsutomu Miyoshi, Shinji Momoi
Corresponding Author
Tsutomu Miyoshi
Available Online January 2014.
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.
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Volume Title
Proceedings of the 2013 International Conference on Advances in Intelligent Systems in Bioinformatics
Series
Advances in Intelligent Systems Research
Publication Date
January 2014
ISBN
978-94-6252-000-4
ISSN
1951-6851
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  -