密度不敏感的近邻传播聚类算法研究  

Research on Density-insensitive Affinity Propagation Clustering Algorithm

作　　者：冯晓磊[1] 于洪涛[1] 

机构地区：[1]国家数字交换系统工程技术研究中心,郑州450002

出　　处：《计算机工程》2012年第2期159-162,共4页Computer Engineering

基　　金：国家"863"计划基金资助项目(2008AA011002;2011AA010603)

摘　　要：近邻传播算法在非凸形、密度不均匀的数据集上很难得到理想的聚类结果。为此,基于核聚类的思想,将数据集非线性地映射到高维空间,使数据集更加分离。利用共享最近邻的相似度度量方法,提出一种密度不敏感的近邻传播算法DIS-AP,以弥补原算法易受特征集维数和密度影响的缺点,从而有效解决数据集非凸和密度不均匀问题,拓宽算法的应用范围。仿真实验结果证明,DIS-AP算法具有更好的聚类性能。To solve the problem that Affinity Propagation（AP） algorithm has poor performance on non-convex and asymmetrical density dataset,kernel clustering is introduced into algorithm.The dataset in kernel space are farther separable through non-linear mapping.Then a similarity measure with shared nearest neighbor is imported,and a density insensitive-affinity propagation algorithm named Density-insensitive Affinity Propagation（DIS-AP） is proposed.DIS-AP overcomes the shortcoming of original AP based on Euclidean distance that is easily influenced by the dimension and density of dataset.It can effectively solve the problem of clustering non-convex and asymmetrical density dataset,and developed its applied range.Experimental results show that this algorithm has better clustering effect.

关 键 词：近邻传播 相似度度量 核聚类 共享最近邻 聚类分析 密度不敏感 

分 类 号：TN915[电子电信—通信与信息系统]