Three-Way Decision Clustering Method Combining Nearest Neighbor Idea and K-means
TANG Xin
Abstract：Aiming at the problems of randomly selecting cluster centers and being easily affected by extreme values in K-means algorithms,a three branch decision clustering method based on the nearest neighbor idea and K-means is proposed.First-ly,using the relationship between sample points,the object with the highest density is obtained as the initial clustering center.Based on the density of neighbors between the remaining sample points and the initial clustering center,suitable clustering objects are selected,and the clustering center is updated at the same time.Then,starting from the farthest Euclidean distance,the article searches for n-1 clustering centers and their corresponding clustering objects,obtaining the results of two branch clustering.Final-ly,combining the three branch decision and nearest neighbor thinking,the above results are further divided into core domain,boundary domain,and trivial domain to obtain the clustering results of the three branch decision.Experiments are conducted on both the UCI dataset and the artificial simulation dataset,and the results show that compared to other methods,this method im-proves clustering accuracy and has stability.
Keywords：K-means algorithmlocal neighbor densityQ-nearest neighborsthree-way decisionthree-way clustering
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 314-319 )