Clustering with kernel-based self-organized maps trained with supervised bias
Abstract
Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in a kernel space. Gaussian kernels are used and their mean and variance components are adapted in order to optimize the fitness to the input density. The KSDG-SOM also grows dynamically up to a size defined with statistical criteria. It is capable of incorporating a priori information for the known categories. This information forms a supervised bias at the cluster formation and the model owns the potentiality of revising incorrect functional labels. The new method overcomes the main drawbacks of most of the existing clustering methods that lack a mechanism for dynamical extension on the basis of a balance between unsupervised and supervised drives.
References
[1]
H. Liu, L. Wong, "Data Mining Tools for Biological Sequences", Journal of Bioinformatics and Computational Biology, Vol. 1, No. 1, p. 139-168, April 2003.
[2]
Eisen Michael B., Spellman Paul T., Patrick O. Brown, and David Botstein, "Cluster analysis and display of genome-wide expression patterns", Proc. Natl. Acad. Sci. USA, Vol. 95, pp. 14863-14868, December 1998.
[3]
Mavroudi Seferina, Papadimitriou Stergios, Bezerianos Anastasios, "Gene Expression Analysis with a Dynamically Extended Self-Organized Map that Exploits Class Information", Bioinformatics, Vol. 18, no 11, 2002, p 1446-1453.
[4]
Papadimitriou S., Mavroudi S., Vladutu L., Bezerianos A., "Ischemia Detection with a Self Organizing Map Supplemented by Supervised Learning", IEEE Trans. On Neural Networks, Vol. 12, No. 3, May 2001, p. 503-515.
[5]
Herrero Javier, Valencia Alfonso, and Dopazo Joaquin, "A hierarchical unsupervised growing neural network for clustering gene expression patterns", Bioinformatics, (2001) Vol. 17, no. 2, pp. 126-136.
[6]
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S. and Golub, T.R. (1999) "Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic diffierentiation", Proc. Natl. Acad. Sci., USA, 92, pp. 2907-2912.
[7]
Friedman, N., M. Linial, I. Nachman, and D'Peier, "Using Bayesian networks to analyze expression data", J. Comp. Bio. 7, 2000, 601-620.
[8]
Fritzke Bernd, "Growing Grid - a self organizing network with constant neighborhood range and adaptation strength", Neural Processing Letters, Vol. 2, No. 5, pp. 9-13, 1995.
[9]
Van Hulle, N.M., "Kernel-Based Topographic Map Formation", Neural Computation, Vol. 14, No 7, p. 1560-1573, 2002.
[10]
Van Hulle, N.M., "Kernel-based equiprobabilistic topographic map formation, Neural Computation, Vol. 10, No. 7, p. 1847-1871, 2002.
[11]
Vesanto Juha Alhoniemi, Esa, "Clustering of the Self-Organized Map", IEEE Transactions on Neural Networks, Vol. 11, No. 3, May 2000, p. 586-600.
[12]
Si J., Lin S., Vuong M. A., "Dynamic topology representing networks", Neural Networks, 13, pp. 617-627, 2000.
[13]
Cheng Guojian and Zell Andreas, "Externally Growing Cell Structures for Data Evaluation of Chemical Gas Sensors", Neural Computing & Applications, 10, pp. 89-97, Springer-Verlag, 2001.
[14]
Campos Marcos M., Carpenter Gail A., "S-TREE: self-organizing trees for data clustering and online vector quantization", Neural Networks 14 (2001), pp. 505-525.
[15]
Van Hulle, N.M., "Joint Entropy Maximization in Kernel-Based Topographic Maps", Neural Computation, Vol. 14, No 8, p. 1887-1906, 2002.
[16]
James R. Williamson, "Self-Organization of Topographic Mixture Networks Using Attentional Feedback", Neural Computation 13:563-593, 2001.
[17]
Bernhard Scholkopf, Alexander J. Smola, "Learning with Kernels: Support Vector Machines, Regularization and Beyond", MIT Press 2002.
[18]
B. Scholkopf, A. J. Smola, R. C. Williamson, P. L. Bartlett, "New support vector algorithms", Neural Computation:1207-1245, 2000.
[19]
Janne Sinkkonen, Samuel Kaski, "Clustering Based on Conditional Distributions in an Auxiliary Space", Neural Computation, 14:217-239, 2001.
[20]
A. Sierra, F. Corbacho, "Reclassification as Supervised Clustering", Neural Computation 12:2537-2546, 2000.
[21]
Bishop, C. M., Svensen, M., Williams, C. K., "GTM: The generative topographic mapping", Neural Computation, 10:215-234, 1998.
[22]
Andras, P., "Kernel-Kohonen networks", International Journal of Neural Systems, Vol. 12, No. 2 (2002) 117-135.
[23]
Ian H. Witten, Eibe Frank, Data Mining, Morgan Kaufmann Publishers, 2000.
[24]
Kohonen T., Self-Organized Maps, Springer-Verlag, Second Edition, 1997.
[25]
Haykin S, Neural Networks, Prentice Hall International, Second Edition, 1999.
[26]
Troyanskaya Olga, Cantor Michael, Shelock Gavin, Brown Pat, Hastie Trevor, Tibshirani Robert, Botstein David, Altman Russ B., "Missing value estimation methods for DNA microarrays", Bioinformatics, Vol. 17, no 6, 2001.
[27]
Lawrence Hunter, Ronald C. Taylor, Sonia M. Leach and Richard Simon, "GEST: a gene expression search tool based on a novel Bayesian similarity metric", Bioinformatics, Vol. 17, Suppl. 1, p. S115-S122.
- Clustering with kernel-based self-organized maps trained with supervised bias
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World Scientific and Engineering Academy and Society (WSEAS)
Stevens Point, Wisconsin, United States
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Published: 27 May 2006
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