Abstract
Clustering technique is an effective tool for medical data analysis as it can work for disease prediction, diagnosis record mining, medical image segmentation, and so on. This paper studies the kernel-based clustering method which can conduct nonlinear partition on input patterns and addresses two challenging issues in unsupervised learning environment: feature relevance estimate and cluster number selection. Specifically, a kernel-based competitive learning paradigm is presented for nonlinear clustering analysis. To distinguish the relevance of different features, a weight variable is associated with each feature to quantify the feature’s contribution to the whole cluster structure. Subsequently, the feature weights and cluster assignment are updated alternately during the learning process so that the relevance of features and cluster membership can be jointly optimized. Moreover, to solve the problem of cluster number selection, the cooperation mechanism is further introduced into the presented learning framework and a new kernel clustering algorithm which can automatically select the most appropriate cluster number is educed. The performance of proposed method is demonstrated by the experiments on different medical data sets.
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References
Ahalt, S.C., Krishnamurty, A.K., Chen, P., Melton, D.E.: Competitive learning algorithms for vector quantization. Neural Networks 3(3), 277–291 (1990)
Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40(3), 825–838 (2007)
Cheung, Y.M.: A competitive and cooperative learning approach to robust data clustering. In: Proceedings of IASTED International Conference on Neural Networks and Computational Intelligence, pp. 131–136 (2004)
Cheung, Y.M.: Maximum weighted likelihood via rival penalized em for density mixture clustering with automatic model selection. IEEE Transactions on Knowledge and Data Engineering 17(6), 750–761 (2005)
Cheung, Y.M.: On rival penalization controlled competitive learning for clustering with automatic cluster number selection. IEEE Transactions on Knowledge and Data Engineering 17(11), 1583–1588 (2005)
Filippone, M., Camastra, F., Masulli, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognition 41, 176–190 (2008)
Hamerly, G., Elkan, C.: Learning the k in k-means. In: Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS), pp. 281–288 (2003)
Huang, D.S., Zhao, X.M., Huang, G.B., Cheung, Y.M.: Classifying protein sequences using hydropathy blocks. Pattern Recognition 39(12), 2293–2300 (2006)
Inokuchi, R., Miyamoto, S.: Lvq clustering and som using a kernel function. In: Proceedings of IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1497–1500 (2004)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31(8), 651–666 (2010)
Ma, J., Wang, T.: A cost-function approach to rival penalized competitive learning (rpcl). IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics 36(4), 722–737 (2006)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)
Render, R.A., Walker, H.F.: Mixture densities, maximum likelihood and the em algorithm. SIAM Review 26(2), 195–239 (1984)
Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of relieff and rrelieff. Machine Learning 53(1), 23–69 (2003)
Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)
Wolf, L., Shashua, A.: Feature selection for unsupervised and supervised inference: the emergence of sparsity in a weight-based approach. Journal of Machine Learning Research 6, 1855–1887 (2005)
Xu, L., Krzyzak, A., Oja, E.: Rival penalized competitive learning for clustering analysis, rbf net, and curve detection. IEEE Transactions on Neural Networks 4(4), 636–648 (1993)
Zeng, H., Cheung, Y.M.: A new feature selection method for gaussian mixture clustering. Pattern Recognition 42, 243–250 (2009)
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Jia, H., Cheung, Ym. (2013). Feature Weighted Kernel Clustering with Application to Medical Data Analysis. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_50
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DOI: https://doi.org/10.1007/978-3-319-02753-1_50
Publisher Name: Springer, Cham
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