# A Survey on Multi-View Clustering

arXiv: https://arxiv.org/abs/1712.06246 · HTML: https://mlsanity.com/paper/1712.06246/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 30 of 32)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (2023)
- [Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity](https://mlsanity.com/paper/2203.11572/) (2022)
- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [Localized Sparse Incomplete Multi-view Clustering](https://mlsanity.com/paper/2208.02998/) (2022)
- [Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding](https://mlsanity.com/paper/2312.08697/) (2023)
- [Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering](https://mlsanity.com/paper/2203.08060/) (2022)
- [Consistent Multiple Graph Embedding for Multi-View Clustering](https://mlsanity.com/paper/2105.04880/) (2021)
- [Joint Multi-view Unsupervised Feature Selection and Graph Learning](https://mlsanity.com/paper/2204.08247/) (2022)
- [Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning](https://mlsanity.com/paper/2209.04187/) (2022)
- [Scalable Multi-view Clustering with Graph Filtering](https://mlsanity.com/paper/2205.09228/) (2022)
- [MORI-RAN: Multi-view Robust Representation Learning via Hybrid Contrastive Fusion](https://mlsanity.com/paper/2208.12545/) (2022)
- [Subspace-Contrastive Multi-View Clustering](https://mlsanity.com/paper/2210.06795/) (2022)
- [Syntactic Multi-view Learning for Open Information Extraction](https://mlsanity.com/paper/2212.02068/) (2022)
- [Trusted Mamba Contrastive Network for Multi-View Clustering](https://mlsanity.com/paper/2412.16487/) (2024)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Kernel Correlation-Dissimilarity for Multiple Kernel k-Means Clustering](https://mlsanity.com/paper/2403.03448/) (2024)
- [Interpretable Multi-View Clustering](https://mlsanity.com/paper/2405.02644/) (2024)
- [URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2407.09120/) (2024)
- [Multi-view Spectral Clustering on the Grassmannian Manifold With Hypergraph Representation](https://mlsanity.com/paper/2503.06066/) (2025)
- [Granular-Ball-Induced Multiple Kernel K-Means](https://mlsanity.com/paper/2506.18637/) (2025)
- [Fine-grained Graph Learning for Multi-view Subspace Clustering](https://mlsanity.com/paper/2201.04604/) (2022)
- [Spectral Clustering of Categorical and Mixed-type Data via Extra Graph Nodes](https://mlsanity.com/paper/2403.05669/) (2024)
- [Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models](https://mlsanity.com/paper/2506.12156/) (2025)
- [Multimodal Classification: Current Landscape, Taxonomy and Future Directions](https://mlsanity.com/paper/2109.09020/) (2021)
- [TCGF: A unified tensorized consensus graph framework for multi-view representation learning](https://mlsanity.com/paper/2309.09987/) (2023)
- [Interpretable Multi-View Clustering Based on Anchor Graph Tensor Factorization](https://mlsanity.com/paper/2404.00883/) (2024)
- [Discriminative community detection for multiplex networks](https://mlsanity.com/paper/2410.00724/) (2024)
- [Joint Tensor and Inter-View Low-Rank Recovery for Incomplete Multiview Clustering](https://mlsanity.com/paper/2503.02449/) (2025)
- [Time-Frequency Filtering Meets Graph Clustering](https://mlsanity.com/paper/2510.07503/) (2025)

## Abstract (arXiv)

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.