Abstract
The visualization of the clusters obtained by a partitioning procedure is very informative as this helps to a better overview of the contents of a data table. For co-clustering, the latent block mixture model is very effective. We propose to define generative self-organizing maps with this model for Gaussian blocks. A perspective is the analysis and the visualization of continuous data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-organizing maps. Springer (1997)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recognition Letters 3(8), 651–666 (2010)
Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: A principled alternative to the self-organizing map. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems 9, pp. 354–360. The MIT Press, Cambridge (1997)
Kabán, A., Girolami, M.: A combined latent class and trait model for analysis and visualisation of discrete data. IEEE Trans. Pattern Anal. and Mach. Intell., 859–872 (2001)
Tino, P., Nabney, I.: Hierarchical gtm: Constructing localized nonlinear projection manifolds in a principled way. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 639–656 (2002)
Vellido, A.: Selective smoothing of the generative topographic mapping. IEEE Transactions on Neural Networks 14(3), 847–852 (2003)
McLachlan, G.J., Peel, D.: Finite Mixture Models. John Wiley and Sons, New York (2000)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Hautaniemi, S., Yli-Harja, O., Astola, J., Kauraniemi, P., Kallioniemi, A., Wolf, M., Ruiz, J., Mousses, S., Kallioniemi, O.-P.: Analysis and Visualization of Gene Expression Microarray Data in Human Cancer Using Self-Organizing Maps. Mach. Learn. 52(1-2), 45–66 (2003)
Newman, A.M., Cooper, J.B.: AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number. BMC Bioinformatics 11, 117 (2010)
Shannon, W., Culverhouse, R., Duncan, J.: Analyzing microarray data using cluster analysis. Pharmacogenomics 4(1), 41–52 (2003)
Govaert, G., Nadif, M.: Clustering with block mixture models. Pattern Recognition 36(2), 463–473 (2003)
Nadif, M., Govaert, G.: Model-Based Co-clustering for Continuous Data. In: ICMLA, pp. 175–180. IEEE Computer Society (2010)
Dempster, A., Laird, N., Rubin, D.: Maximum-likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. Ser. B 39, 1–38 (1977)
Govaert, G., Nadif, M.: An EM algorithm for the block mixture model. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 643–647 (2005)
Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, vol. 118. Springer (1996)
Yamaguchi, N.: Variational bayesian inference with automatic relevance determination for generative topographic mapping. In: SCIS-ISIS 2012, pp. 2124–2129 (2012)
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., Levine, A.J.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. USA 96(12), 6745–6750 (1999)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2), 224–227 (1979)
Rousseeuw, P.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Priam, R., Nadif, M., Govaert, G. (2013). Gaussian Topographic Co-clustering Model. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-41398-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41397-1
Online ISBN: 978-3-642-41398-8
eBook Packages: Computer ScienceComputer Science (R0)