Abstract
Clustering for the analysis of the gene expression profiles has been used for identifying the functions of the genes and of unknown genes. Since the genes usually belong to multiple functional families, fuzzy clustering methods are more appropriate than the conventional hard clustering methods. However, it is still required to devise natural way to measure the quality of the cluster partitions that are obtained by fuzzy clustering. In this paper, a Bayesian validation method of selecting a fuzzy partition with the largest posterior probability given the dataset is proposed to evaluate the fuzzy partitions effectively. Analysis of yeast cell-cycle data follows to show the usefulness of the proposed method.
This research was supported by the Ubiquitous Computing Research Program funded by the Ministry of Information and Communication of Korea
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References
Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3(11), research 0059.1–0059.22 (2002)
Bezdeck, J.C.: Cluster validity with fuzzy sets. J. Cybernit. 3, 58–72 (1974)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 13(8), 841–846 (1991)
Fukuyama, Y., Sugeno, M.: A new method of choosing the number of clusters for the fuzzy c-means method. In: Proceedings of 5th Fuzzy Systems Symposium, pp. 247–250 (1989)
Kim, D.W., Lee, K.H., Lee, H.: Fuzzy cluster validation index based on inter-cluster proximity. Pattern Recognition Letters 24, 2561–2574 (2003)
Yeung, K.Y., et al.: Validating clustering for gene expression data. Bioinformatics 17(4), 309–318 (2001)
Bolshakova, N., Azuaje, F.: Cluster validation techniques for genome expression data. SIGPRO 21(82), 1–9 (2002)
Gasch, A.P., Eisen, M.B.: Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3(11), research 0059.1–0059.22 (2002)
Dembele, D., Kastner, P.: Fuzzy c-means method for clustering microarray data. Bioinformatics 19(8), 973–980 (2003)
Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7(6), 673–679 (2001)
Cho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Gabrielian, A.E., Landsman, D., Lockhart, D.J., Davis, R.W.: A genomewide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 65–73 (1998)
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Kim, KJ., Yoo, SH., Cho, SB. (2005). Bayesian Validation of Fuzzy Clustering for Analysis of Yeast Cell Cycle Data. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_110
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DOI: https://doi.org/10.1007/11553939_110
Publisher Name: Springer, Berlin, Heidelberg
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