Spectral Clustering Based on Density Coefficient and Shared Nearest Neighbors
ZHANG Tao,GE Hongwei
1（Ministry of Education Key Laboratory of Advanced Process Control for Light Industry,Jiangnan University,Wuxi 214122,China）2（School of Internet of Things,Jiangnan University,Wuxi 214122,China）
Abstract：The selftuning spectral clustering algorithm uses the adaptive Gauss kernel to calculate the similarity cannot get correct results on complex datasets,a spectral clustering based on density coefficient and shared nearest neighbors is proposed in this paper.Firstly,the density coefficient of the points are calculated and the adaptive kernel parameters are calculated based on the weight.Then,the mutual K nearest neighbor graph is optimized based on the threshold value and the number of shared nearest neighbors are calculated.Finally,the similarity is calculated based on the number of shared nearest neighbors and kernel parameters and clustering.Experiments on artificial and realworld datasets show that the proposed algorithm can obtain a better clustering result in dealing with the complex datasets.
张涛,葛洪伟. 基于密度系数和共享近邻的谱聚类[J]. 小型微型计算机系统, 2017, 38(8): 1829-1833.
ZHANG Tao,GE Hongwei. Spectral Clustering Based on Density Coefficient and Shared Nearest Neighbors. Journal of Chinese Computer Systems, 2017, 38(8): 1829-1833.
2017, Vol. 38 
