Multi-View Clustering: A New Perspective on Cluster Analysis Integrating Multivariate Data

In the era of big data, with the increasing enrichment of data acquisition methods, multi-view data has emerged in large quantities, making multi-view clustering algorithms a current research hotspot. Multi-view clustering aims to comprehensively integrate data information from multiple different perspectives to achieve accurate clustering. It is based on the principle of consistency, exploring the similarity of data clustering structures under different views to verify the reliability of results; at the same time, it follows the principle of complementarity, integrating the unique information of each view, making up for the limitations of single-view, and improving the accuracy of clustering. According to the mechanisms and principles involved in multi-view clustering algorithms, they can be divided into five categories: co-training algorithms, multi-kernel learning algorithms, multi-view graph clustering algorithms, multi-view subspace clustering algorithms, and multi-task multi-view clustering algorithms. Currently, although multi-view clustering has achieved remarkable results, it still faces challenges, such as difficulties in judging the correctness of views and insufficient capabilities in handling incomplete data. Future research needs to focus on overcoming these problems to promote the in-depth application of multi-view clustering in various fields, including bioinformatics, social media, e-commerce, etc., and help data mining and knowledge discovery reach new heights.