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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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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