The recent developments in the research area of multi-view data analysis and classification have shown a significant shift towards more integrated and efficient approaches. Researchers are increasingly focusing on combining multiple methodologies to leverage the strengths of each, while mitigating their individual limitations. This trend is evident in the introduction of novel frameworks that integrate granular computing with traditional classification models, enhancing both robustness and computational efficiency. Additionally, there is a growing emphasis on consistency and diversity in multi-view unsupervised feature and instance co-selection, which aims to improve downstream task performance by effectively managing dimensionality and sample size. Clustering ensemble methods are also advancing, with new techniques that balance efficiency and robustness by formulating the problem as a k-HyperEdge Medoids discovery. Furthermore, multi-view clustering is benefiting from unified approaches that merge multi-kernel learning with matrix factorization, offering a more streamlined and computationally efficient solution. These advancements collectively push the boundaries of current methodologies, offering more versatile and powerful tools for handling complex, multi-source data in various domains.