A novel graph-based clustering method using noise cutting

Abstract

Recently, many methods have appeared in the field of cluster analysis. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns. Algorithms based on graphs provide good results for this problem. However, some widely used graph-based clustering methods, such as spectral clustering algorithms, are sensitive to noise and outliers. In this paper, a cut-point clustering algorithm (CutPC) based on a natural neighbor graph is proposed. The CutPC method performs noise cutting when a cut-point value is above the critical value. Normally, the method can automatically identify clusters with arbitrary shapes and detect outliers without any prior knowledge or preparatory parameter settings. The user can also adjust a coefficient to adapt clustering solutions for particular problems better. Experimental results on various synthetic and real-world datasets demonstrate the obvious superiority of CutPC compared with k-means, DBSCAN, DPC, SC, and DCore.