Density peaks clustering with second-order K-nearest neighbors and multi-cluster merging
In the face of manifold data,the local density of density peaks clustering(DPC)algorithm is easy to find the wrong cluster center and the allocation strategy is easy to cause the residual samples far from the cluster center to be misallocation.In view of the above problems,this paper proposes density peaks clustering with second-order K-nearest neighbors and multi-cluster merging.Firstly,the minimum second-order K-nearest neighbor is used to define the local density,highlighting the density difference between the cluster center and the non-cluster center,so as to find the correct cluster center;Secondly,the K-nearest neighbor is used to find the local representative points of the sample and determine the core points,and the core points are used to guide the micro-cluster division;Finally,the inter-cluster attraction defined by the minimum second-order K-nearest neighbor and shared nearest neighbor is used to merge the micro-clusters,which avoids the misallocation of samples away from the cluster center,and the micro-cluster merging process does not require iteration.In this paper,DPC-SKMM algorithm is compared with IDPC-FA,DPCSA,FNDPC,FKNN-DPC,DPC algorithm.Experimental results show that DPC-SKMM algorithm can cluster manifolds and UCI data sets effectively.
density peaks clusteringmanifold datasecond-order K-nearest neighborsK-nearest neighborattractivenessmulti-cluster merging strategy
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