By using sample's natural nearest neighbors,a three-way clustering algorithm is proposed based on sample's perturbation theory.The proposed algorithm combines natural nearest neighbor information with sample's perturbation to generate two datasets.By randomly selecting parts of the sample's feature,different clustering results are obtained through K-means clustering algorithms.The stability of each sample is calculated based on the defined frequencies.The universe is divided into stable set and unstable set based on the sample's stability.Then,we use different strategies to obtain the core region and fringe region of each cluster.The testing results on five open datasets verify the effectiveness of the proposed algorithm through comparative tests with two traditional clustering methods.
NETL
NSTL
万方数据
