Self-Organizing Map Clustering Analysis for Molecular Data

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.

Authors and Affiliations

1. School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, 100876, China

   Lin Wang & Yinghua Lu

2. Lab. of Computational Linguistics, School of Humanities and Social Sciences, Tsinghua University, Beijing, 100084, China

   Minghu Jiang

3. Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Im Neuenheimer Feld 368, 69210, Heidelberg, Germany

   Lin Wang, Minghu Jiang, Frank Noe & Jeremy C. Smith

Editors and Affiliations

1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

   Jun Wang

2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

   Zhang Yi

3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

   Jacek M. Zurada

4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

   Bao-Liang Lu

5. School of Electrical and Electronic Engineering, University of Manchester, UK

   Hujun Yin

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