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.
出处
《计算机工程》
CAS
CSCD
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)
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
Affinity Propagation（AP）
similarity measurement
kernel clustering
shared nearest neighbor
clustering analysis
density insensitive
