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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.
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References
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Authors and Affiliations
School of Electronics Eng., Beijing Univ. of Post and Telecom., Beijing, 100876, China
Lin Wang & Yinghua Lu
Lab. of Computational Linguistics, School of Humanities and Social Sciences, Tsinghua University, Beijing, 100084, China
Minghu Jiang
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
- Lin Wang
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- Minghu Jiang
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- Yinghua Lu
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- Frank Noe
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- Jeremy C. Smith
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Editors and Affiliations
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China
Jun Wang
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China
Zhang Yi
Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A
Jacek M. Zurada
Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China
Bao-Liang Lu
School of Electrical and Electronic Engineering, University of Manchester, UK
Hujun Yin
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, L., Jiang, M., Lu, Y., Noe, F., Smith, J.C. (2006). Self-Organizing Map Clustering Analysis for Molecular Data. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_185
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