Self-Organizing map clustering analysis for molecular data

Authors:
Lin Wang;Minghu Jiang;Yinghua Lu;Frank Noe;Jeremy C. Smith
Affiliations:
School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, China;Lab. of Computational Linguistics, School of Humanities and Social Sciences, Tsinghua University, Beijing, China;School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, China;Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany;Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

In this paper hierarchical clustering and self-organizing maps (SOM) clustering are compared by using molecular data of large size sets. The hierarchical clustering can represent a multi-level hierarchy which show the tree relation of cluster distance. SOM can adapt the winner node and its neighborhood nodes, it can learn topology and represent roughly equal distributive regions of the input space, and similar inputs are mapped to neighboring neurons. By calculating distances between neighboring units and Davies-Boulding clustering index, the cluster boundaries of SOM are decided by the best Davies-Boulding clustering index. The experimental results show the effectiveness of clustering for molecular data, between-cluster distance of low energy samples from transition networks is far bigger than that of "local sampling" samples, the former has a better cluster result, "local sampling" data nevertheless exhibit some clusters.