Multi View Clustering

Multi-view clustering aims to integrate information from multiple data sources (views) to improve the accuracy and robustness of clustering algorithms. Current research focuses on developing efficient and scalable methods, particularly those leveraging deep learning architectures like autoencoders and graph neural networks, to handle high-dimensional data and address challenges like missing data and view heterogeneity. These advancements are significant for improving the analysis of complex datasets across various domains, including medical imaging and action recognition, where multiple data modalities provide complementary information. Furthermore, there is a growing emphasis on developing interpretable models to enhance the trustworthiness and understandability of multi-view clustering results.