# Interpretable Multi-View Clustering

arXiv: https://arxiv.org/abs/2405.02644 · HTML: https://mlsanity.com/paper/2405.02644/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 15 of 15)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Deep Model Reassembly](https://mlsanity.com/paper/2210.17409/) (2022)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization](https://mlsanity.com/paper/1709.01212/) (2017)
- [Adaptive Graph Auto-Encoder for General Data Clustering](https://mlsanity.com/paper/2002.08648/) (2020)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Explainable k-means. Don't be greedy, plant bigger trees!](https://mlsanity.com/paper/2111.03193/) (2021)
- [Interpretable Clustering via Multi-Polytope Machines](https://mlsanity.com/paper/2112.05653/) (2021)
- [Meta Decision Trees for Explainable Recommendation Systems](https://mlsanity.com/paper/1912.09140/) (2019)

## Built on by (most cited first, 2 of 2)

- [Interpretable Clustering: A Survey](https://mlsanity.com/paper/2409.00743/) (2024)
- [Deep Skew-t Mixture Models](https://mlsanity.com/paper/2609.00773/) (2026)

## Abstract (arXiv)

Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view clustering framework. Our method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that our method not only provides a transparent clustering process for multi-view data but also delivers performance comparable to state-of-the-art multi-view clustering methods. To the best of our knowledge, this is the first effort to design an interpretable clustering framework specifically for multi-view data, opening a new avenue in this field.