Ensemble Learning for Cluster Number Detection Based on Shared Nearest Neighbor Graph and Spectral Clustering

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.