Manifold data is composed of several clusters,each with a distinctive arc shape.Samples of the same cluster are characterized by large distances between them.The density peaks clustering(DPC)algorithm is simple and efficient,but it does not perform well when dealing with manifold data for the following reasons:the two-density metrics of the algorithm may result in different degrees of missing information,and its allocation strategy only considers distance and density factors,which can lead to poor clustering accuracy.We proposed a DPC based on weighted natural nearest neighbors for manifold datasets(DPC-WNNN)algorithm to address the above issues.DPC-WNNN comprehensively analyzed the local and global information of the sample when designing the definition of local density,and intro-duced weighted natural nearest neighbors and inverse nearest neighbors to address the problem of miss-ing information in Gaussian or cutoff kernels.The sample assignment was calculated by introducing the idea of shared reverse nearest neighbors and shared nearest neighbors to compensate for the lack of spa-tial factors in the original algorithm.The experimental results were compared with the seven algorithms in the manifold and real datasets,and show that the DPC-WNNN algorithm can find the center of clus-ters more effectively and assign samples accurately,which shows excellent clustering performance.
density peakclusteringmanifold datanatural neighbor
维普
万方数据
