密度不敏感的近邻传播聚类算法研究

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。

Research on Density-insensitive Affinity Propagation Clustering Algorithm

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.