Journal of Jilin University Science Edition ›› 2022, Vol. 60 ›› Issue (5): 1176-1182.
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LIU Ming, YU Ziqi
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Abstract: Aiming at the problem that the hybrid Gaussian model using the traditional expectation-maximum algorithm for parameter estimation was too dependent on the initial probability density center for the final clustering effect, we proposed an improved expectation-maximum algorithm based on the fuzzy C-means algorithm for parameter initialization. The experimental results show that compared with the traditional unsupervised clustering algorithms (the fuzzy C-means algorithm, the K-means algorithm and the unmodified expectation-maximum algorithm), the improved expectation-maximum algorithm has better clustering performance metrice and better global clustering effect than the traditional clustering algorithm in a practical user knowledge level clustering task.
Key words: fuzzy C-means, expectation-maximum algorithm, unsupervised clustering, Gaussian mixture model
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LIU Ming, YU Ziqi. An Improved Expectation-Maximum Algorithm[J].Journal of Jilin University Science Edition, 2022, 60(5): 1176-1182.
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http://xuebao.jlu.edu.cn/lxb/EN/Y2022/V60/I5/1176
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