河北工业科技2018，Vol.35Issue(2)：77-83,7.DOI:10.7535/hbgykj.2018yx02001
一种基于自适应相似矩阵的谱聚类算法
A spectral clustering algorithm based on adaptive similarity matrix
摘要
Abstract
In order to eliminate the fluctuation of the scale parameters in gaussian kernel function in constructing the similarity matrix of spectral clustering algorithm,a self-adaptive similarity matrix is constructed and applied in the spectral clustering algorithm.Geodesic distance measure is used in distance measure between data points in the adaptive similarity matrix.Distance between points closer to each other is approximately equal to the Euclidean distance,while for distance between two points far-ther away,each data's k-nearest neighbors are firstly obtained by Euclidean distance,then the geodesic distances of the nearest neighbors are accumulated,thus,the shortest distance between each pair of data can be get.The local density of two points is defined by the shared neighbor,reflecting the eigen structure of the data set better.Finally,experiments on both five artificial data sets and five UCI data sets show that the proposed method is more accurate than the others,and has a strong adaptive ability for complex distribution data.The research provides idea and method for data mining and machine learning.关键词
应用数学/相似矩阵/谱聚类/密度/测地距离Key words
applied mathematics/similar matrix/spectral clustering/density/geodesic distance分类
信息技术与安全科学引用本文复制引用
王贝贝,杨明,燕慧超,孙笑仙..一种基于自适应相似矩阵的谱聚类算法[J].河北工业科技,2018,35(2):77-83,7.基金项目
国家自然科学基金(61601412,61571404,61471325) （61601412,61571404,61471325）
山西省自然科学基金(2015021099) （2015021099）
