Reliable Clustering Number Estimation for Contrastive Multi-View Clustering

Abstract

In recent years, contrastive multi-view clustering has achieved remarkable performance improvements. However, existing methods still face two key challenges: (1) reliance on a predefined number of clusters k, which is often unknown in real-world scenarios; and (2) contrastive learning might cause representation degeneration when thecollected multiple views inherently have inconsistent semantic information . To address these issues, we propose a novel framework--Reliable Clustering Number Estimation for Contrastive Multi-View Clustering (RCNMC). RCNMC consists of a Semantics-Aware Contrastive Learning module and a Reinforcement Learning-based Cluster Number Learning module. Specifically, the Semantics-Aware Contrastive Learning module first measures the discrepancy between pairwise representations and adaptively strengthens useful pairwise views while weakening unreliable ones, thereby alleviating representation degeneration. The Reinforcement Learning-based Cluster Number Learning module infers the optimal number of clusters in an unsupervised manner by using intra-cluster and inter-cluster distances as a reward-driven strategy. The two modules complement each other, making RCNMC more suitable for complex multi-view clustering tasks in real-world scenarios. Extensive experiments on multiple benchmark datasets demonstrate that RCNMC significantly outperforms existing state-of-the-art methods.