Open Access

ARTICLE

An Improved Algorithm of K-means Based on Evolutionary Computation

by Yunlong Wang, Xiong Luo, Jing Zhang, Zhigang Zhao, Jun Zhang

1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China
3 Key Laboratory of Wind Energy and Solar Energy Technology (Inner Mongolia University of Technology), Ministry of Education, Hohhot, 010051, China
4 Shunde Graduate School, University of Science and Technology Beijing, Foshan, 528399, China
5 Science and Technology Division, North China Institute of Science and Technology, Beijing, 101601, China

* Corresponding Author: Xiong Luo. Email:

Intelligent Automation & Soft Computing 2020, 26(5), 961-971. https://doi.org/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 some extent.

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Citations