有效距离在聚类算法中的应用*
光俊叶1，刘明霞1,2，张道强1+
1.南京航空航天大学计算机科学与技术学院，南京211106
2.泰山学院信息科学技术学院，山东泰安271021
Application of Effective Distance in Clustering Algorithms *
GUANG Junye 1,LIU Mingxia 1,2,ZHANG Daoqiang 1+
1.College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
2.College of Information Science and Technology,Taishan University,Taian,Shandong 271021,China
+Corresponding author:E-mail:dqzhang@https://www.doczj.com/doc/d815379284.html,
GUANG Junye,LIU Mingxia,ZHANG Daoqiang.Application of effective distance in clustering algorithms.Journal of Frontiers of Computer Science and Technology,2017,11(3)：406-413.
Abstract:Distance metric learning is a key step in clustering analysis,which is an important sub-domain of data mining.Euclidean distance metric is a quite commonly used local distance metric in clustering algorithms,which only focuses on the distance between two samples.This paper proposes a new global distance metric method,named as the effec-tive distance metric.In the new method,the similarity between two samples is evaluated by using not only the distance between these two samples,but also distances between one specific sample and all the other related ones.Sparse recon-struction coefficients are employed to reflect such global relationship among samples.Then,this paper develops three effective distance-based clustering algorithms,including E K -means,E K -medoids and EFCM,by applying the effective distance to three classical clustering algorithms,i.e.,K -means,K -medoids and FCM (fuzzy C-means),respectively.The experimental results on UCI benchmark datasets demonstrate the efficacy of the proposed methods.
Key words:clustering;distance metric;metric learning;effective distance
*The National Natural Science Foundation of China under Grant Nos.61422204,61473149(国家自然科学基金);the Open Founda-tion of Graduate Innovation Center in NUAA under Grant No.kfjj20151605(南京航空航天大学研究生创新实验室开放基金).Received 2016-02,Accepted 2016-04.
CNKI 网络优先出版:2016-04-19,https://www.doczj.com/doc/d815379284.html,/kcms/detail/11.5602.TP.20160419.1143.006.html
ISSN 1673-9418CODEN JKYTA8
Journal of Frontiers of Computer Science and Technology
1673-9418/2017/11(03)-0406-08
doi:10.3778/j.issn.1673-9418.1603046E-mail:fcst@https://www.doczj.com/doc/d815379284.html, https://www.doczj.com/doc/d815379284.html, Tel:+86-10-89056056万方数据