A General Domain-Adaptive Clustering Approach via Cross-Dataset Prototype Alignment

Deep clustering typically learns cluster structures independently for each dataset, limiting its ability to exploit shared patterns across heterogeneous domains. We propose CPC, a general domain-adaptive clustering approach based on cross-dataset prototype alignment, which jointly learns a shared prototype space across multiple domains. By allowing different domains to activate shared prototypes, CPC establishes cross-dataset structural correspondence and discovers representative prototypes that serve as compact cluster anchors. Experiments on 11 heterogeneous real-world datasets show that CPC achieves the best or tied-best accuracy on 9 datasets and substantially outperforms existing clustering methods, with improvements of over 40 percentage points on challenging datasets such as Primary Tumor. These results demonstrate the effectiveness of shared prototype spaces for domain-adaptive clustering.