Abstract
In this paper, we propose a novel hierarchical clustering method based on evolutionary strategies. This method leads to gene expression data analysis, and shows its effectiveness with regard to other clustering methods through cluster validity measures on the results. Additionally, a novel visual validation interactive tool is provided to carry out visual analytics among clusters of a dendrogram. This interactive tool is an alternative for the used validity measures. The method introduced here attempts to solve some of the problems faced by other hierarchical methods. Finally, the results of the experiments show that the method can be very effective in the cluster analysis on DNA microarray data.
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Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman, Inc., Amsterdam (1989)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, New York (1999)
Eisen, M., Spellman, T., Brown, P., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, USA 95, 14863–14868 (1998)
Korte, B., Vygen, J.: DHC: A density-based hierarchical clustering method for time series gene expression data. In: Proceedings of the Third IEEE Symposium on BioInformatics and BioEngineering, BIBE (2003)
Ma, P.C.H., Chan, K.C.C., Yao, X., Chiu, D.K.Y.: An evolutionary clustering algorithm for gene expression microarray data analisys. IEEE Transactions on Evolutionary Computation 10, 296–314 (2006)
Berrar, D.P., Dubitzky, W., Granzow, M.: A Practical Approach to Microarray Data Analysis. Kluwer Academic Publishers, Dordrecht (2003)
Jolliffe, I.T.: Principal Component Analysis. Springer, Heidelberg (2002)
Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: VIS 1990: Proceedings of the 1st conference on Visualization 1990, pp. 361–378 (1990)
Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs (1998)
Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: A survey 16, 1370–1386 (2004)
Greene, W.A.: Unsupervised hierarchical clustering via a genetic algorithm. In: Congress on Evolutionary Computation, CEC 2003, vol. 2, pp. 998–1005. IEEE, Los Alamitos (2003)
Handl, J., Knowles, J., Kell, D.B.: Computational cluster validation in post-genomic data analysis, vol. 21, pp. 3201–3212. Oxford University Press, Oxford (2005)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. In: An Introduction to Clustering Analysis, John Wiley & Sons, Inc., Hoboken (2005)
Chipman, H., Hastie, T., Tibshirani, R.: Clustering microarray data. Statistical Analysis of Gene Expression Microarray Data (2003)
Sorlie, T., et al.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98, 10969–10974 (2001)
Chipman, H., Tibshirani, R.: Hybrid hierarchical clustering with applications to microarray data. Biostatistics 7, 302–317 (2006)
Macnaughton-Smith, P., Williams, W.T., Dale, M.B., Mockett, L.G.: Dissimilarity analysis: a new technique of hierarchical subdivision. Nature 202, 1034–1035 (1965)
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Castellanos-Garzón, J.A., García, C.A., Miguel-Quintales, L.A. (2009). An Evolutionary Hierarchical Clustering Method with a Visual Validation Tool. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_46
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DOI: https://doi.org/10.1007/978-3-642-02478-8_46
Publisher Name: Springer, Berlin, Heidelberg
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