|本期目录/Table of Contents|

[1]杨 洁,王国胤*,庞紫玲.密度峰值聚类相关问题的研究[J].南京大学学报(自然科学),2017,53(4):791.[doi:10.13232/j.cnki.jnju.2017.04.023]
 Yang Jie,Wang Guoyin*,Pang Ziling.Relative researches of clustering by fast search and find of density peaks[J].Journal of Nanjing University(Natural Sciences),2017,53(4):791.[doi:10.13232/j.cnki.jnju.2017.04.023]





Relative researches of clustering by fast search and find of density peaks
杨 洁12王国胤1*庞紫玲1
Yang Jie12Wang Guoyin1*Pang Ziling1
1.Chongqing Key Laboratory of Computational Intelligence,Chongqing University of
Posts and Telecommunications,Chongqing,400065,China;
2.School of Physics and Electronic Science,Zunyi Normal College,Zunyi,563002,China
密度聚类密度峰值粒计算图 论
density clusteringdensity peaks clustering(DPC)granular computinggraph theory
相对于其他的密度聚类算法,密度峰值聚类(Density Peaks Clustering,DPC)算法思想简洁新颖,所需参数少,不需要进行迭代求解,而且具有可扩展性.但是,DPC仍然具有一定缺陷,例如存在截断阈值dc的定义模糊以及选取中心点失效等问题.在阐述了DPC的算法思想和原理的基础上,分析了DPC算法的缺陷,然后从多个改进的角度对其相关研究工作进行了综述.通过分析DPC与相关理论(数据场、图论、粒计算等)的联系,针对密度峰值的缺点,提出了基于粒计算的DPC算法改进框架,其中包括由细到粗、由细到粗和双向变粒度这三种机制以及基于网格粒化的密度峰值算法框架.最后对DPC今后的研究工作进行了展望,包括动态密度峰值聚类、利用密度峰值研究网络拓扑、处理复杂任务以及改进其他聚类等,希望为DPC的进一步研究提供新思想
Compared with other clustering algorithms,density peaks clustering(DPC) algorithm possess a novel and concise idea,which requires fewer parameters and iteration,besides it is scalable.However,there are also some defects,such as the definition of dc and selection of the centers.In this paper,the principle of DPC is introduced and the defects of DPC are analyzed firstly.Based on the related key problems,main theoretical researches are surveyed in various views.Besides,the relationship between DPC and relevant theory is also analyzed,i.e.data field,graph theory and granular computing.Especially,to address these issue of DPC,three mechanisms as follows:from fine to coarse,from coarse to from,and bi-directional variable granularity,which are presented based on the advantages of granular computing.The mechanism of DPC based on mesh granulation is also presented.Finally,the existing challenge problems are analyzed,and the research directions of DPC in the future are explored,including dynamic density peak clustering,the research of network topology by DPC,complex tasks solving by DPC,and the improvement of other clustering algorithms using DPC,et al,which provide valuable reference for researchers and data analysts who are concerned with clustering.


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更新日期/Last Update: 2017-08-03