An Improved K-Means Algorithm Based on Initial Clustering Center Optimization

作     者：LI Taihao NAREN Tuya ZHOU Jianshe REN Fuji LIU Shupeng 

作者机构：Beijing Advanced Innovation Center for Imaging TechnologyCapital Normal UniversityBeijing 100048China Flatley Discovery LabBoston 02129USA Department of Information Science&Intelligent SystemsUniversity of TokushimaTokushima 7708506Japan School of Communication and Information EngineeringShanghai UniversityShanghai 200072China 

出 版 物：《ZTE Communications》 (中兴通讯技术（英文版）)

年 卷 期：2017年第15卷第B12期

页      面：43-46页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：clustering K-means algorithm initial clustering center 

摘      要：The K-means algorithm is widely known for its simplicity and fastness in text clustering.However,the selection of the initial clus?tering center with the traditional K-means algorithm is some random,and therefore,the fluctuations and instability of the clustering results are strongly affected by the initial clustering center.This paper proposed an algorithm to select the initial clustering center to eliminate the uncertainty of central point selection.The experiment results show that the improved K-means clustering algorithm is superior to the traditional algorithm.