A Three-step Method for Three-way Clustering by Similarity-based Sample’s Stability
Jin Song Zhu, Dongqin Jiang, Pingxin Wang · Mathematical Problems in Engineering · 2022
Clustering is an important research field in machine learning. Traditional clustering approaches are not very effective in dealing with clusters having overlapping regions. To better capture the three types of relationships between a cluster and a sample, namely, belong-to fully, belong-to partially and not belong-to fully, we propose a theory of similarity-based sample’s stability and develop a three-step method for three-way clustering by integrating similarity-based sample’s stability into the idea of three-way clustering in this paper. In the proposed theory, the similarity of two samples is used to define the frequencies of two samples and the samples stability is calculated based on the defined frequencies and determinacy function. With this stability, the universe is divided into stable set and unstable set. The samples in the stable set are assigned into the core region of each cluster by using traditional clustering algorithm. The samples in the unstable set are assigned into the fringe region of corresponding cluster according to distances between the elements and the centers of the cluster core regions. Therefore, a three-way clustering is naturally formed. Experimental results on datasets show that this method can improve the structure of the clustering results.