Abstract
The concept of cluster stability is introduced to assess the validity of data partitionings found by clustering algorithms. It allows us to explicitly quantify the quality of a clustering solution, without being dependent on external information. The principle of maximizing the cluster stability can be interpreted as choosing the most self-consistent data partitioning. We present an empirical estimator for the theoretically derived stability index, based on resampling. Experiments are conducted on well known gene expression data sets, re-analyzing the work by Alon et al. [1] and by Spellman et al. [8].
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References
U. Alon, N. Barkai D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci., 96:6745–6750, 1999.
J. Breckenridge. Replicating cluster analysis: Method, consistency and validity. Multivariate Behavioral research, 1989.
J. Fridlyand & S. Dudoit. Applications of resampling methods to estimate the number of clusters and to improve the accuracy of a clustering method. Stat. Berkeley Tech Report. No. 600, 2001.
E. Levine, E. Domany. Resampling Method for Unsupervised Estimation of Cluster Validity. Neural Computation 13: 2573–2593, 2001.
D. H. Mack, E.Y. Tom, M. Mahadev, H. Dong, M. Mittman, S. Dee, A. J. Levine, T. R. Gingeras, D. J. Lockhart. In: Biology of Tumors, eds. K. Mihich, C. Croce, (Plenum, New York), pp. 123, 1998.
C.H. Papadimitriou & K. Steiglitz. Combinatorial Optimization, Algorithms and Complexity, Prentice-Hall, Englewood Cliffs, NJ, 1982.
K. Rose, E. Gurewitz and G. Fox. Vector Quantization and Deterministic Annealing, IETrans. Inform. Theory, Vol. 38, No. 4, pp. 1249–1257, 1992.
P.T. Spellman, G. Sherlock, MQ. Zhang, V.R. Iyer, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein, B. Futcher. Comprehensive Identification of Cell Cycle-regulated Genes of the Yeast Saccharomyces cerevisiae by Microarray Hybridization. Molecular Biology of the Cell 9, 3273–3297, 1998.
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© 2002 Springer-Verlag Berlin Heidelberg
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Roth, V., Braun, M.L., Lange, T., Buhmann, J.M. (2002). Stability-Based Model Order Selection in Clustering with Applications to Gene Expression Data. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_99
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DOI: https://doi.org/10.1007/3-540-46084-5_99
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