Abstract
Fuzzy clustering is an important tool for analyzing microarray cancer data sets in order classify the tissue samples. This article describes a real-coded Genetic Algorithm (GA) based fuzzy clustering method that combines with popular Artificial Neural Network (ANN) / Support vector Machine (SVM) based classifier in this purpose. The clustering produced by GA is refined using ANN / SVM classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray cancer data sets. The performance of the proposed clustering method has been compared to several other microarray clustering algorithms for three publicly available benchmark cancer data sets, viz., leukemia, Colon cancer and Lymphoma data to establish its superiority.
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Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gassenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomeld, D.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Alizadeh, A.A., Eisen, M.B., Davis, R., Ma, C., Lossos, I., Rosenwald, A., Boldrick, J., Warnke, R., Levy, R., Wilson, W., Grever, M., Byrd, J., Botstein, D., Brown, P.O., Straudt, L.M.: Distinct types of diffuse large b-cell lymphomas identified by gene expression profiling. Nature 403, 503–511 (2000)
Yeung, K.Y., Bumgarner, R.E.: Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biology 4 (2003)
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. Proceedings of National Academy of Science, Cell Biology 96, 6745–6750 (1999)
Bandyopadhyay, S., Mukhopadhyay, A., Maulik, U.: An improved algorithm for clustering gene expression data. Bioinformatics 23(21), 2859–2865 (2007)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs (1988)
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1996)
MacKay, D.J.C.: The evidence framework applied to classification networks. Neural Computation 4(5), 720–736 (1992)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. J. Machine Learning Research 2, 265–292 (2001)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E., Golub, T.: Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Nat. Academy of Sciences 96, 2907–2912 (1999)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, New York (1989)
Maulik, U., Bandyopadhyay, S.: Genetic algorithm based clustering technique. Pattern Recognition 33, 1455–1465 (2000)
Maulik, U., Bandyopadhyay, S.: Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Transactions on Geoscience and Remote Sensing 41(5), 1075–1081 (2003)
Andersen, L.N., Larsen, J., Hansen, L.K., HintzMadsen, M.: Adaptive regularization of neural classifiers. In: Proc. IEEE Workshop on Neural Networks for Signal Processing VII, New York, USA, pp. 24–33 (1997)
Sigurdsson, S., Larsen, J., Hansen, L.: Outlier estimation and detection: Application to skin lesion classification. In: Proc. Intl. Conf. on Acoustics, Speech and Signal Processing (2002)
Yeung, K.Y., Ruzzo, W.L.: An empirical study on principal component analysis for clustering gene expression data. Bioinformatics 17(9), 763–774 (2001)
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comp. App. Math. 20, 53–65 (1987)
Kim, S.Y., Lee, J.W., Bae, J.S.: Effect of data normalization on fuzzy clustering of dna microarray data. BMC Bioinformatics 7(134) (2006)
Dembele, D., Kastner, P.: Fuzzy c-means method for clustering microarray data. Bioinformatics 19(8), 973–980 (2003)
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Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S. (2009). Refining Genetic Algorithm Based Fuzzy Clustering through Supervised Learning for Unsupervised Cancer Classification. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2009. Lecture Notes in Computer Science, vol 5483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01184-9_17
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DOI: https://doi.org/10.1007/978-3-642-01184-9_17
Publisher Name: Springer, Berlin, Heidelberg
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