# Dual-disentangled Deep Multiple Clustering

arXiv: https://arxiv.org/abs/2402.05310 · HTML: https://mlsanity.com/paper/2402.05310/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 7 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Generating Sentences from Disentangled Syntactic and Semantic Spaces](https://mlsanity.com/paper/1907.05789/) (2019)
- [Improving Disentangled Text Representation Learning with Information-Theoretic Guidance](https://mlsanity.com/paper/2006.00693/) (2020)
- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Unsupervised Visual Representation Learning by Online Constrained K-Means](https://mlsanity.com/paper/2105.11527/) (2021)
- [AugDMC: Data Augmentation Guided Deep Multiple Clustering](https://mlsanity.com/paper/2306.13023/) (2023)
- [Improving Image Clustering With Multiple Pretrained CNN Feature Extractors](https://mlsanity.com/paper/1807.07760/) (2018)
- [Multi-View Multiple Clusterings using Deep Matrix Factorization](https://mlsanity.com/paper/1911.11396/) (2019)

## Built on by (most cited first, 3 of 3)

- [Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs](https://mlsanity.com/paper/2503.22241/) (2025)
- [Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning](https://mlsanity.com/paper/2405.20852/) (2024)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)

## Abstract (arXiv)

Multiple clustering has gathered significant attention in recent years due to its potential to reveal multiple hidden structures of the data from different perspectives. Most of multiple clustering methods first derive feature representations by controlling the dissimilarity among them, subsequently employing traditional clustering methods (e.g., k-means) to achieve the final multiple clustering outcomes. However, the learned feature representations can exhibit a weak relevance to the ultimate goal of distinct clustering. Moreover, these features are often not explicitly learned for the purpose of clustering. Therefore, in this paper, we propose a novel Dual-Disentangled deep Multiple Clustering method named DDMC by learning disentangled representations. Specifically, DDMC is achieved by a variational Expectation-Maximization (EM) framework. In the E-step, the disentanglement learning module employs coarse-grained and fine-grained disentangled representations to obtain a more diverse set of latent factors from the data. In the M-step, the cluster assignment module utilizes a cluster objective function to augment the effectiveness of the cluster output. Our extensive experiments demonstrate that DDMC consistently outperforms state-of-the-art methods across seven commonly used tasks. Our code is available at https://github.com/Alexander-Yao/DDMC.