基于稠密区域的K-medoids聚类算法

计算机工程与应用 ›› 2016, Vol. 52 ›› Issue (16): 85-89.

基于稠密区域的K-medoids聚类算法

赵湘民1，2，陈曦1，潘楚3

1.长沙理工大学计算机与通信工程学院，长沙 410114
2.长沙商贸旅游职业技术学院，长沙 410004
3.湖南大学信息科学与工程学院，长沙 410082

出版日期:2016-08-15 发布日期:2016-08-12

Novel K-medoids clustering algorithm based on dense regional block

ZHAO Xiangmin1，2, CHEN Xi1, PAN Chu3

1.Institute of Computer and Communication Engineering, Changsha University of Sciences and Technology, Changsha 410114, China
2.Changsha College of Commerce & Tourism, Changsha 410004, China
3.College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China

Online:2016-08-15 Published:2016-08-12

摘要/Abstract

摘要： 针对传统K-medoids聚类算法对初始中心点敏感，以及迭代次数较高等缺点，提出一种可行的初始化方法和中心点搜索更新策略。新算法首先利用密度可达思想为数据集中每个对象建立一个稠密区域，遴选出[K]个密度大且距离较远的稠密区域，把对应的稠密区域的核心对象作为聚类算法的[K]个初始中心点；其次，把[K]个中心点搜索更新范围锁定在所选的[K]个有效稠密区域里。新算法在Iris、Wine、PId标准数据集中测试，获取了理想中心点和稠密区域，并且在较少的迭代次数内收敛到最优解或近似最优解。

关键词: K-medoids聚类算法, 稠密区域, 初始中心点, 中心点搜索更新

Abstract: In view of the traditional K-medoids clustering algorithm is sensitive to the initial center, as well as the shortcoming of high number of iterations, put forward a feasible initialization method and a center search update strategy. New algorithm firstly using the density-reachable thought to establish a dense regional block for each object of the data set, select [K] dense regional blocks which their densities are larger and the distance are far away for each selected dense regional blocks, put the core object of the corresponding dense regional blocks as the K initial centers；Secondly, the centers search update scope is locking the [K] selected effective dense regional blocks. Tested on Iris, Wine and PId standard data sets, this new algorithm obtains ideal initial centers and dense regional blocks, what’s more, converges to the optimal solution or approximate optimum solution within less number of iterations.

Key words: K-medoids clustering algorithm, dense regional block, initial center, center search update

赵湘民1，2，陈曦1，潘楚3. 基于稠密区域的K-medoids聚类算法[J]. 计算机工程与应用, 2016, 52(16): 85-89.

ZHAO Xiangmin1，2, CHEN Xi1, PAN Chu3. Novel K-medoids clustering algorithm based on dense regional block[J]. Computer Engineering and Applications, 2016, 52(16): 85-89.