@Article{iasc.2020.010128,
AUTHOR = {Yunlong Wang, Xiong Luo, Jing Zhang, Zhigang Zhao, Jun Zhang},
TITLE = {An Improved Algorithm of K-means Based on Evolutionary Computation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {5},
PAGES = {961--971},
URL = {http://www.techscience.com/iasc/v26n5/40817},
ISSN = {2326-005X},
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 some extent.},
DOI = {10.32604/iasc.2020.010128}
}