自动化学报2016，Vol.42Issue(9)：1401-1412,12.DOI:10.16383/j.aas.2016.c150864
基于密度峰值的聚类集成
Clustering Ensemble Based on Density Peaks
摘要
Abstract
Clustering ensemble aims to improve the accuracy, stability and robustness of clustering results. A good ensemble result is achieved by integrating multiple base clustering results. This paper proposes a clustering ensemble model based on density peaks. First, this paper discovers that the base clustering results can be expressed with density after studying and analyzing the existing clustering algorithms and models. Second, rapid computation of the maximal information coefficient (RapidMic) is introduced to represent the correlation of the base clustering results, which is then used to measure the density of these original datasets after base clustering. Third, the density peak (DP) algorithm is improved for clustering ensemble. Furthermore, some standard datasets are used to evaluate the proposed model. Experimental results show that our model is effective and greatly outperforms some classical clustering ensemble models.关键词
聚类集成/近邻传播/密度峰值/相似性矩阵Key words
Clustering ensemble/affinity propagation/density peaks/similarity matrix引用本文复制引用
褚睿鸿,王红军,杨燕,李天瑞..基于密度峰值的聚类集成[J].自动化学报,2016,42(9):1401-1412,12.基金项目
国家科技支撑计划课题(2015BAH19F02),国家自然科学基金(61262058,61572407),教育部在线教育研究中心在线教育研究基金(全通教育)(2016YB158),西南交通大学中央高校基本科研业务费专项基金(A0920502051515-12)资助Supported by National Science and Technology Support Pro-gram (2015BAH19F02), National Natural Science Foundation of China (61262058,61572407), Online Education Research Cen-ter of the Ministry of Education Online Education Research Fund (Full Education)(2016YB158) and Fundamental Research Funds for the Central Universities of Southwest Jiaotong Uni-versity (A0920502051515-12) （2015BAH19F02）
