Deep Clustering via Community Detection
Journal:
arXiv
Published Date:
Jan 3, 2025
Abstract
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.
