计算机技术与发展2017，Vol.27Issue(10)：60-64,5.DOI:10.3969/j.issn.1673-629X.2017.10.013
多维数据K-means谱聚类算法改进研究
Research on Modification of K-means Spectral Clustering Algorithm of Multidimensional Data
摘要
Abstract
Aiming at the problem that the traditional K-means algorithm cannot determine the initial cluster number k automatically and spectral clustering algorithm is sensitive to parameter,a new K -means algorithm based on spectral clustering called PK-means is pro-posed. It makes improvement and innovation in selection of k values,sorts the calculated high density data points orderly,and then picks out the frontal 96% density point to cluster,so that the number of clusters k can be obtained with high accuracy. In the meantime,it also selects the unaffected and higher stable similarity measure method based on spectral clustering fuzziness and uses the FCM algorithm for membership degree matrix so as to determine the similarity between data points. The PK-means, K -means and DSSC have been em-ployed to deal with multi-dimensional nonlinear datasets. The experimental results show that whether the selected data source is low di-mension or high dimension,the efficiency of K-means is the lowest,followed by DSSC,and PK-means owns obvious advantages which always has the higher clustering accuracy and stronger robustness than the traditional clustering algorithm. The higher the dimension,the more prominent the clustering performance.关键词
K-means算法/谱聚类算法/聚类/FCM算法/隶属度矩阵Key words
K-means algorithm/spectral clustering algorithm/clustering/FCM algorithm/degree of membership matrix分类
信息技术与安全科学引用本文复制引用
谢志明,王鹏,黄焱..多维数据K-means谱聚类算法改进研究[J].计算机技术与发展,2017,27(10):60-64,5.基金项目
国家自然科学基金资助项目(60702075) （60702075）
广东省科技厅高新技术产业化科技攻关项目(2011B010200007) （2011B010200007）
广东省高等职业教育质量工程教育教学改革项目(GDJG2015244,GDJG2015245) （GDJG2015244,GDJG2015245）
