Clustering by Fast Search and Find of Density Peaks with Data Field

作     者：WANG Shuliang WANG Dakui LI Caoyuan LI Yan DING Gangyi 

作者机构：International School of SoftwareWuhan University School of SoftwareBeijing Institute of Technology 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2016年第25卷第3期

页      面：397-402页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：supported by the National Natural Science Foundation of China(No.61173061,No.61472039,No.71201120) the Doctoral Fund of Higher Education(No.20121101110036) 

主　　题：Data field Potential entropy Big data clustering Optimal threshold value Automatic extraction 

摘      要：A clustering algorithm named Clustering by fast search and find of density peaks is for finding the centers of clusters quickly. Its accuracy excessively depended on the threshold, and no efficient way was given to select its suitable value, i.e., the value was suggested be estimated on the basis of empirical experience. A new way is proposed to automatically extract the optimal value of threshold by using the potential entropy of data field from the original dataset. For any dataset to be clustered, the threshold can be calculated from the dataset objectively instead of empirical estimation. The results of comparative experiments have shown the algorithm with the threshold from data field can get better clustering results than with the threshold from empirical experience.