Research on a K-Means Clustering Algorithm Based on Improved Differential Evolution
WANG Fengling
LIANG Haiying
ZHANG Bo
Abstract：The difference and the shortcomings of K-means algorithm are sensitive to the initial value and easily fall into the local optimal solution. The difference evolution algorithm has strong global convergence ability and robustness,but its convergence rate is slow. In view of the above problems and defects,this paper first introduces the steps and the concrete process of evolutionary algorithm key operation and differential evolution algorithm. Then,the description,steps and concrete flow of K-means clustering algorithm based on differential evolution are described. Finally,a K-means clustering algorithm based on improved differential evo?lution is proposed,and the improvement scheme,the steps and the concrete flow of the algorithm are introduced in detail. The K-means clustering algorithm based on differential evolution and improved algorithm is used to simulate the experiment. The experi?mental results show that the algorithm has better searching ability,and the algorithm is faster and more robust.
Keywords：differential evolutionK-means algorithmK-means clustering algorithm
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 1042-1048 )