Abstract
We proposed a hybrid clustering algorithm based on the improved particle swarm optimization algorithm and EM clustering algorithm to overcome the shortcomings of EM algorithm, which is sensitive to initial value and easy to sink into local minimum. First, get the optimal clustering number of any dataset to obtain the initial parameter of mixed model with the improved PSO algorithm, whose inertia weight increased and decreased along the fold line automatically. Then build the mixed density model of image data by multiple iterations of the EM algorithm. Finally divide all the pixel value of the image into corresponding branch of hybrid model with the Bayesian criterion to get the classification of image data. The proposed algorithm can increase the diversity of EM clustering algorithm initialization and promote optimization search in the global scope. Experimental results of simulation prove its accuracy and validity.
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References
Yao, H., Duan, Q., Li, D., Wang, J.: An improved k-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling 58(3), 790–798 (2013)
Zhao, F., Fan, J., Liu, H.: Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation. Expert Systems with Applications 41(9), 4083–4093 (2014)
Wu, P., Liu, Y., Li, Y., Shi, Y.: Trus image segmentation with non-parametric kernel density estimation shape prior. Biomedical Signal Processing and Control 8(6), 764–771 (2013)
Nguyen, T.M., Jonathan Wu, Q., Mukherjee, D., Zhang, H.: A finite mixture model for detail-preserving image segmentation. Signal Processing 93(11), 3171–3181 (2013)
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97(458), 611–631 (2002)
Ueda, N., Nakano, R., Ghahramani, Z., Hinton, G.E.: Smem algorithm for mixture models. Neural Computation 12(9), 2109–2128 (2000)
Verbeek, J.J., Vlassis, N., Kröse, B.: Efficient greedy learning of gaussian mixture models. Neural Computation 15(2), 469–485 (2003)
Zhao, Q., Hautamäki, V., Kärkkäinen, I., Fränti, P.: Random swap em algorithm for gaussian mixture models. Pattern Recognition Letters 33(16), 2120–2126 (2012)
Melnykov, V., Melnykov, I.: Initializing the em algorithm in gaussian mixture models with an unknown number of components. Computational Statistics & Data Analysis 56(6), 1381–1395 (2012)
Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2010)
Melnykov, V., Maitra, R., et al.: Finite mixture models and model-based clustering. Statistics Surveys 4, 80–116 (2010)
Liu, Z., Xiao, J.-G., Song, Y.-Q.: Image segmentation based on non-parametric mixture model of legendre orthogonal polynomial. Jisuanji Yingyong Yanjiu 27(8), 3165–3167 (2010)
Abraham, A., Das, S., Roy, S.: Swarm intelligence algorithms for data clustering. In: Soft Computing for Knowledge Discovery and Data Mining, pp. 279–313. Springer (2008)
Niasar, N.S., Yazdani, S., Mohajeri, M.: K-nichepso clustering. In: 2008 International Conference on Machine Learning and Cybernetics, pp. 2668–2672. IEEE (2008)
Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Applied Soft Computing 11(4), 3658–3670 (2011)
Van der Merwe, D., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: The 2003 Congress on Evolutionary Computation, CEC 2003, vol. 1, pp. 215–220. IEEE (2003)
Chen, D.-H., Liu, Z.-J., Wang, Z.-H.: Improved possibilistic c-means clustering algorithm based on particle swarm optimization. Computer Science 39(11), 122–126 (2012)
Yang, M.-S., Lai, C.-Y., Lin, C.-Y.: A robust em clustering algorithm for gaussian mixture models. Pattern Recognition 45(11), 3950–3961 (2012)
Radivojac, P., Clark, W.T., Oron, T.R., Schnoes, A.M., Wittkop, T., Sokolov, A., Graim, K., Funk, C., Verspoor, K., Ben-Hur, A., et al.: A large-scale evaluation of computational protein function prediction. Nature Methods 10(3), 221–227 (2013)
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Tang, Z., Song, YQ., Liu, Z. (2014). Medical Image Clustering Based on Improved Particle Swarm Optimization and Expectation Maximization Algorithm. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_38
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DOI: https://doi.org/10.1007/978-3-662-45643-9_38
Publisher Name: Springer, Berlin, Heidelberg
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