Kernel-based adaptation for affinity propagation clustering algorithm
基于核自适应的近邻传播聚类算法

AP algorithm has become increasingly popular in recent years as an efficient and fast clustering algorithm.AP has better performance on large and multi-class dataset than the existing clustering algorithms.But for the datasets with complex cluster structures,it cannot produce good clustering results.Through analyzing the property of data clusters,this paper proposed a kernel function,optimized that the parameters automatically according to the dataset structure,and the dataset in kernel space were linearly separable or almost linearly.Carried AP on the kernel space,it had a kernel-adaptive affinity propagation clustering algorithm(KA-APC).Compared with the original AP clustering,it had the advantages of effectively dealing with the large multi-scale dataset.The promising experimental results show that this algorithm outperforms the original AP algorithm.