Density Peaks Clustering Algorithm Based on Hybrid Density and Micro-clusters Aggregation
Density peaks clustering algorithm is a simple and efficient new clustering algorithm,but it is difficult to find the correct cluster centers when dealing with datasets with uneven density distribution,and it is easy to appear errors in the process of sample allocation,resulting in poor clustering effect.To solve these problems,a density peaks clustering algorithm based on hybrid density and micro-clusters aggregation(HMDPC)is proposed.The HMDPC algorithm first defines the hybrid density of the samples according to the reverse K nearest neighbor and the attribution relationship between the samples.Secondly,the data is divided into multiple micro-clusters and the similarity between the micro-clusters is defined.Based on this similarity,multiple micro-clusters are aggregated to obtain the final clustering result.Experiments were carried out on synthetic datasets and UCI datasets,and HMDPC algorithm was compared with other 6 clustering algorithms.The experimental results showed that HMDPC algorithm had better clustering effect.
density peaks clusteringreverse K nearest neighborhybrid densitymicro-clusters aggregation
万方数据
维普
