Self-Adaptive Clustering of Dynamic Multi-Graph Learning

Abstract

In the process of graph clustering, the quality requirements for the structure of data graph are very strict, which will directly affect the final clustering results. Enhancing data graph is the key step to improve the performance of graph clustering. In this paper, we propose a self-adaptive clustering method to obtain a dynamic fine-tuned sparse graph by learning multiple static original graph with different sparsity degrees. By imposing a constrainted rank on the corresponding Laplacian matrix, the method utilizes the eigenvectors of the Laplacian matrix to create a new and simple data sparse matrix to have exactly k connected components, so that the method can quickly and directly learn the clustering results. The experimental results on synthetic and multiple public datasets verify that the proposed method is meaningful and beneficial to discover the real cluster distribution of datasets.