1 Hong Kong Baptist University, 999077, Hong Kong Special Administrative Region of China; Guangdong Provincial/Zhuhai Key Laboratory IRADS and Department of Computer Science, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087, Guangdong, China. Electronic address: u430201707@mail.uic.edu.cn.
2 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H3G 1T7, QC, Canada. Electronic address: zhiwen.luo@mail.concordia.ca.
3 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, H3G 1T7, QC, Canada. Electronic address: nizar.bouguila@concordia.ca.
4 Guangdong Provincial/Zhuhai Key Laboratory IRADS and Department of Computer Science, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087, Guangdong, China. Electronic address: wfsu@uic.edu.cn.
5 Guangdong Provincial/Zhuhai Key Laboratory IRADS and Department of Computer Science, Beijing Normal-Hong Kong Baptist University, Zhuhai, 519087, Guangdong, China. Electronic address: wentaofan@uic.edu.cn.

Multi-view clustering has gained significant attention due to its ability to integrate data from diverse perspectives, frequently outperforming single-view approaches. However, existing methods often assume a Gaussian distribution within the latent embedding space, which can degrade performance when handling high-dimensional data or data with complex, non-Gaussian distributions. These limitations complicate effective data alignment, hinder meaningful information fusion across views, and impair accurate similarity measurement. To overcome these challenges, we propose a novel contrastive multi-view clustering framework that leverages hyperspherical embeddings by explicitly modeling the latent space using the von Mises-Fisher (vMF) distribution. Additionally, the framework incorporates a contrastive learning paradigm guided by alignment and uniformity losses, facilitating more discriminative and disentangled representations within the hyperspherical latent space. Specifically, the alignment loss enhances consistency across embeddings of different views from the same instance, while the uniformity loss ensures distinctiveness among embeddings from different samples within each cluster. By jointly optimizing these objectives, our method substantially improves intra-cluster cohesion and inter-cluster separability across multiple views. Extensive experiments conducted on several benchmark datasets confirm that the proposed approach significantly outperforms state-of-the-art methods, particularly in scenarios involving high-dimensional and complex datasets. The source code of our model is publicly accessible at https://github.com/jcdh/DRMVC.