A robust clustering method with noise identification based on directed K-nearest neighbor graph

Abstract

Obtaining the optimal cluster number and generating reliable clustering results in nonlinear manifolds are necessary but challenging tasks. Most existing clustering algorithms have considerable limitations in dealing with local and nonlinear data patterns, while graph-based clustering has shown impressive performance in identifying clusters in such data patterns. In this paper, we propose a robust clustering method with noise cutting based on directed k-nearest neighbor graph (CDKNN) to identify the desired cluster number automatically and produce reliable clustering results simultaneously on nonlinear, non-overlapping but locally tight-connected data patterns. This method draws support from the k-nearest neighbor graph to represent the complex nonlinear datasets and applies parameter adaptive process to make the proposed clustering method better adapt to specific data patterns. The proposed method is robust to the noises of arbitrary shape datasets because it uses a directed K-nearest neighbor to cut out sparse nodes. We use simulation and UCI real-world datasets to prove the validity of the innovatory method by comparing it to k-means, DBSCAN, OPTICS, AP, SC, and CutPC algorithms in terms of clustering ACC, ARI, NMI, and FMI. The experimental results confirm that the proposed method outperforms the alternative nonlinear clustering methods.