Constructing Superior Representations Beyond the Original Documents via a Contrastive Gaussian Fusion Network for Clustering

Abstract

Document clustering plays an important role in text mining and information retrieval. Existing methods primarily focus on document-intrinsic features, overlooking dataset-level features and consequently failing to construct superior representations. We propose a Contrastive Gaussian Fusion Network (CGFN) that can construct superior representations beyond the original documents. Specifically, CGFN fuses the Gaussian distributions of neighbor-derived information and intrinsic textual features in the latent space. By incorporating contrastive learning into the fusion process, our proposed method is able to learn high-quality representations while simultaneously mitigating noise and minimizing information loss. Experiments on four real-world datasets demonstrate that CGFN outperforms state-of-the-art methods, achieving superior clustering by robustly capturing holistic distributions and neighbor patterns.