Improvement of visual stability by adjusting initial map of SOM and using hierarchical clustering
Tsutomu Miyoshi, Shinji Momoi
Available Online January 2014.
- self-organizing map, feature maps, visual stability, improvement method, clustering.
- 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.
- 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 -