Deep Clustering via Community Detection

Deep clustering is an essential task in modern artificial intelligence, aiming to partition a set of data samples into a given number of homogeneous groups (i.e., clusters). Even though many Deep Neural Network (DNN) backbones and clustering strategies have been proposed for the task, achieving increasingly improved performance, deep clustering remains very challenging due to the lack of accurately labeled samples. In this paper, we propose a novel approach of deep clustering via community detection. It initializes clustering by detecting many communities, and then gradually expands clusters by community merging. Compared with the existing clustering strategies, community detection factors in the new perspective of cluster network analysis. As a result, it has the inherent benefit of high pseudo-label purity, which is critical to the performance of self-supervision. We have validated the efficacy of the proposed approach on benchmark image datasets. Our extensive experiments have shown that it can effectively improve the SOTA performance. Our ablation study also demonstrates that the new network perspective can effectively improve community pseudo-label purity, resulting in improved clustering performance.