GRPO-based Cluster Decision Agent for Unknown-$\boldsymbol{K}$ Multi-view Clustering

Existing contrastive multi-view clustering methods rely on a pre-defined cluster number, limiting their flexibility in real-world scenarios lacking prior knowledge. To address this, we propose GROK, a novel framework driven by a cluster decision agent for unknown-$K$ multi-view clustering. It pioneers the adaptation of group relative policy optimization (GRPO) —a reinforcement learning strategy for LLM reasoning— into the unsupervised domain to autonomously determine the optimal $K$. Specifically, the agent orchestrates the clustering process through three synergistic phases. First, in the state perception phase, we employ a structure-aware adaptive backbone to aggregate multi-view data, providing the agent with consistent and discriminative consensus observations. Second, in the group decision phase, we introduce an action space divide-and-conquer strategy and an adaptive reward function. Equipped with these mechanisms, the agent performs group sampling and relative advantage estimation within the discrete action space of candidate $K$ values, autonomously searching for the optimal $K$ via reward maximization. Finally, via geometric feedback, geometric clustering guidance mechanism transforms the agent's structural hypotheses into explicit differentiable constraints to reshape feature manifolds, thereby closing the perception-decision-feedback loop. Experimental results demonstrate that GROK achieves superior clustering performance in unknown-$K$ scenarios by autonomously exploring the cluster structure.

In many real-world scenarios, the number of natural groups within data is unknown, whereas existing multi-view clustering methods typically require this number to be specified in advance. To address this limitation, we propose GROK, a new framework that can autonomously infer the optimal number of clusters. GROK first extracts consistent and discriminative information from multiple data views, then intelligently explores different candidate cluster numbers, and finally uses feedback from the discovered structure to refine the learned feature representation. Experimental results show that GROK can reliably and accurately uncover latent cluster structures without prior knowledge of the cluster number, while also improving the performance of existing clustering methods. This work offers a more autonomous and flexible paradigm for unsupervised learning, with promising applications in areas such as bioinformatics and multimedia analysis.