Abstract
Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge.We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.
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Au, W.-H., Keith, C.C.C., Andrew, K.C.W., Wang, Y.: Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data. IEEE Trans. Computation Biology and Bioinformatics 2(2), 83–101 (2005)
Xing, E.P., Karp, R.M.: CLIFF: Clustering of High-Dimensional Microarray Data via Iterative Feature Filtering Using Normalized Cuts. Bioinformatics 17(Suppl.1), S306–S315 (2001)
Hastie, T., Tibshirani, R., Eisen, M., Brown, P., Scherf, U., Weinstein, J., Alizadeh, A., Staudt, L., Botstein, D.: Gene Shaving: a New Class of Clustering Methods for Expression Arrays. In: Tech. Report, Stanford University (2000)
Alter, O., Brown, P., Botstein, D.: Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling. Proc. Natl. Acad. Sci. USA, 10101–10106 (2000)
Piatetsky-Shapiro, G., Khabaza, T., Ramaswamy, S.: Capturing Best Practice for Microarray Gene Expression Data Analysis. In: Proc. Ninth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining, pp. 407–415 (2003)
Tamayo, P., Solni, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E.S., Golub, T.R.: Interpreting Patterns of Gene Expression with Self-Organizing Maps: Methods and Application to Hematopoietic Differentiation. Proc. Nat’l academy of Sciences of the United States of Am. 96(6), 2907–2912 (1997)
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. Nat’l Academy of Sciences of the United States of Am. 96(12), 6745–6750 (1999)
Jiang, D., Tang, C., Zhang, A.: Cluster Analysis for Gene Expression Data: A Survey. IEEE Trans. Knowledge and Data Eng. 16(11), 1370–1386 (2004)
Eisen, M., Spellman, P., Brown, P., Botstein, D.: Cluster Analysis and Display of Genome-Wide Expression Patterns. Proc. Natl. Acad. Sci. USA, 14863–14868 (1998)
Heyer, L.J., Kruglyak, S., Yooseph, S.: Exploring Expression Data: Identification and Analysis of Coexpressed Genes. Genome Research 9, 1106–1115 (1999)
Wong, A.K.C., Liu, T.S.: Typicality, Diversity and Feature Patterns of an Ensemble. IEEE Trans. Computers 24(2), 158–181 (1975)
Liu, L., Wong, A.K.C., Wang, Y.: A Global Optimal Algorithm for Class-Dependent Discretization of Continuous Data. Intelligent Data Analysis 8(2), 151–170 (2004)
Jain, A.K., Chandrasekaran, B.: Dimensionality and Sample Size Considerations in Pattern Recognition Practice. In: Krishnaiah, P.P., Kanal, L.N. (eds.) Handbook of Statistics, pp. 835–855. North Holland, Amsterdam (1982)
Raudys, S.J., Jain, A.K.: Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(3), 252–264 (1991)
Xu, L.: Rival Penalized Competitive Learning, Finite Mixture, and Multisets Clustering. In: Proc.1998 IEEE Int. Joint Conf. Neural Networks, vol. 3, pp. 2525–2530 (1998)
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Pei, S., Huang, DS., Li, K., Irwin, G.W. (2006). Gene Selection by Cooperative Competition Clustering. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence and Bioinformatics. ICIC 2006. Lecture Notes in Computer Science(), vol 4115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816102_50
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DOI: https://doi.org/10.1007/11816102_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37277-6
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