Abstract: Mean Shift is a widely used density-based clustering method,but it is greatly affected by bandwidth selection,and the partitioning characteristics based on the number of visits may lead to low clustering accuracy.To address these issues,this paper proposes a fuzzy three-way clustering algorithm based on Mean Shift(FTWMS).By introducing a dynamic drift point selection strategy and a fuzzy membership-based division mechanism,the proposed method optimizes the clustering process of the tradi-tional Mean Shift algorithm.Firstly,the algorithm enhances the drift process by dynamically selecting drift points,which helps identify more reliable cluster centers.In addition,the fuzzy membership is calculated by integrating the distances from data points to different drift points,the visit frequencies of clusters,and the local densities of cluster.Based on this,each cluster is respectively divided into core and boundary regions,yielding the final three-way clustering structure.Finally,experiments are conducted on 6 synthetic datasets and 6 UCI datasets.The results show that the FTWMS algorithm outperforms traditional Mean Shift,K-means and three-way evidence theory-based density peak clustering(3W-PEDP) algorithms in clustering evaluation metrics,including ACC,NMI,and ARI.The algorithm also demonstrates superior ability in delineating cluster boundaries,offering better overall performance than the three comparison algorithms.

Key words: Data mining, Mean shift, Three-way clustering, Fuzzy membership, Local density