Self-Organizing Maps Combined with Eigenmode Analysis for Automated Cluster Identification
Please always quote using this URN: urn:nbn:de:0297-zib-4279
- One of the important tasks in Data Mining is automated cluster analysis. Self-Organizing Maps (SOMs) introduced by {\sc Kohonen} are, in principle, a powerful tool for this task. Up to now, however, its cluster identification part is still open to personal bias. The present paper suggests a new approach towards automated cluster identification based on a combination of SOMs with an eigenmode analysis that has recently been developed by {\sc Deuflhard et al.} in the context of molecular conformational dynamics. Details of the algorithm are worked out. Numerical examples from Data Mining and Molecular Dynamics are included.
Author: | Tobias Galliat, Wilhelm Huisinga, Peter Deuflhard |
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Document Type: | ZIB-Report |
Tag: | Self-Organizing Maps; cluster analysis |
MSC-Classification: | 15-XX LINEAR AND MULTILINEAR ALGEBRA; MATRIX THEORY / 15Axx Basic linear algebra / 15A18 Eigenvalues, singular values, and eigenvectors |
62-XX STATISTICS / 62Hxx Multivariate analysis [See also 60Exx] / 62H30 Classification and discrimination; cluster analysis [See also 68T10] | |
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area) / 68Txx Artificial intelligence / 68T05 Learning and adaptive systems [See also 68Q32, 91E40] | |
Date of first Publication: | 1999/11/26 |
Series (Serial Number): | ZIB-Report (SC-99-38) |
ZIB-Reportnumber: | SC-99-38 |
Published in: | Appeared in: Proc. of the 2nd Intern. ICSC Symposium on Neural Computation, ISCS Academic Press 2000 |