A Three Way Density Peak Clustering Algorithm Combining Grey Wolf Optimization Algorithm and Dynamic Neighborhood
The clustering effect of density peak clustering algorithm is affected by the truncation distance.The value of c has a significant impact and the decision errors caused by the forced partitioning of uncertain data in traditional two-way clustering processing.Therefore,a three-way density peak clustering algorithm combining the Grey Wolf optimization algorithm and dy-namic neighborhood is proposed.Firstly,to address the truncation distance.The problem of difficult selection of c is to use the Silhouette index as the objective function within the cluster,and use the global optimization ability of the Grey Wolf Optimization Algorithm(GWO)to solve the optimal truncation distance;In order to make the division of uncertain data more rea-sonable,combined with the idea of dynamic neighborhood,the K-nearest neighbor algorithm is used to convert the two-way clustering results into three-way clustering results.Through exper-imental verification on artificial datasets and UCI real datasets,the clustering accuracy and over-all performance of this algorithm are superior to the other five comparative algorithms.
density peak clusteringgrey wolf optimization algorithmthree-way clusteringtruncation distance
NETL
NSTL
万方数据
