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    ARTICLE

    An Improved Algorithm of K-means Based on Evolutionary Computation

    Yunlong Wang1,2,3, Xiong Luo1,2,4,*, Jing Zhang1,2,3, Zhigang Zhao1, Jun Zhang5

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 961-971, 2020, DOI:10.32604/iasc.2020.010128

    Abstract K-means is a simple and commonly used algorithm, which is widely applied in many fields due to its fast convergence and distinctive performance. In this paper, a novel algorithm is proposed to help K-means jump out of a local optimum on the basis of several ideas from evolutionary computation, through the use of random and evolutionary processes. The experimental results show that the proposed algorithm is capable of improving the accuracy of K-means and decreasing the SSE of K-means, which indicates that the proposed algorithm can prevent K-means from falling into the local optimum to More >

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