Abstract
Though there exist a lot of cluster ensemble approaches, few of them consider how to degrade the effect of noisy attributes in the dataset. In the paper, we propose a new cluster ensemble framework, named as double self-organizing map based cluster ensemble (SOM 2 CE) to perform clustering on noisy datasets. SOM 2 CE incorporates the self-organizing map (SOM) twice into the ensemble framework to discovery the underlying structure of noisy datasets, which applies SOM to perform clustering not only on the sample dimension, but also on the attribute dimension. SOM 2 CE also adopts the normalized cut algorithm to partition the consensus matrix constructed from multiple clustering solutions, and obtain the final results. Experiments on both synthetic datasets and cancer gene expression profiles illustrate that the proposed approach not only achieves good performance on synthetic datasets and cancer gene expression profiles, but also outperforms most of the existing approaches in the process of clustering gene expression profiles.
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References
Minaei-Bidgoli, B., Topchy, A.P., Punch, W.F.: A Comparison of Resampling Methods for Clustering Ensembles. In: IJCAI 2004, pp. 939–945 (2004)
Weng, F., Jiang, Q., Shi, L., Wu, N.: An Intrusion Detection System Based on the Clustering Ensemble. In: 2007 IEEE International Workshop on Anti-Counterfeiting, Security, Identification, pp. 121–124 (2007)
Fern, X.Z., Brodley, C.E.: Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach. In: ICML 2003, pp. 186–193 (2003)
Yang, Y., Kamel, M., Jin, F.: Clustering Ensemble Using ANT and ART. In: Swarm Intelligence in Data Mining 2006, pp. 243–264 (2006)
Topchy, A.P., Bidgoli, B.M., Jain, A.K., Punch, W.F.: Adaptive Clustering Ensembles. In: ICPR (1) 2004, pp. 272–275 (2004)
Yu, Z., Wong, H.-S., You, J., Yu, G., Han, G.: Hybrid Cluster Ensemble Framework based on the Random Combination of Data Transformation Operators. Pattern Recognition (2011) (to appear)
Dimitriadou, E., Weingessel, A., Hornik, K.: Voting-Merging: An Ensemble Method for Clustering. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 217–224. Springer, Heidelberg (2001)
Topchy, A.P., Jain, A.K., Punch, W.F.: Clustering Ensembles: Models of Consensus and Weak Partitions. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1866–1881 (2005)
Faceli, K., Ferreira de Carvalho, A.C.P.L., Pereira de Souto, M.C.: Multi-objective clustering ensemble. Int. J. Hybrid Intell. Syst. 4(3), 145–156 (2007)
Li, T., Ding, C.H.Q.: Weighted Consensus Clustering. In: SDM 2008, pp. 798–809 (2008)
Kohonen, T.: The Self-Organizing Map. Proceedings of the IEEE 78(9) (1990)
Jianbo, S., Jitendra, M.: Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
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Yu, Z., Chen, H., You, J., Li, L., Han, G. (2012). SOM 2 CE: Double Self-Organizing Map Based Cluster Ensemble Framework and its Application in Cancer Gene Expression Profiles. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_37
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DOI: https://doi.org/10.1007/978-3-642-31087-4_37
Publisher Name: Springer, Berlin, Heidelberg
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