Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering
Weihang Zhang, Xiucai Ye, Testuya Sakurai · 2022 International Joint Conference on Neural Networks (IJCNN) · 2022
Detecting the number of clusters is important for cluster analysis. Many existing methods detect the cluster number by predefining a list of candidate cluster numbers. However, if the candidate cluster numbers are not well predefined, the cluster number cannot be correctly detected. In this paper, we propose a novel clustering method which automatically generates the candidate cluster numbers and the corresponding cluster partitions based on multiple shared nearest neighbor graphs. A shared low-rank similarity matrix is then recovered from the cluster partitions by ensemble learning. Finally, spectral clustering is applied on the shared low-rank similarity matrix with the candidate cluster numbers to detect the cluster number. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method not only correctly detects the cluster numbers, but also obtains better clustering results in comparison to the existing methods.