# Deep k-Means: Jointly clustering with k-Means and learning representations

arXiv: https://arxiv.org/abs/1806.10069 · HTML: https://mlsanity.com/paper/1806.10069/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2018.

## Built on by (most cited first, 30 of 50)

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- [Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering](https://mlsanity.com/paper/2106.09874/) (2021)
- [Deep Embedded K-Means Clustering](https://mlsanity.com/paper/2109.15149/) (2021)
- [Deep Clustering of Tabular Data by Weighted Gaussian Distribution Learning](https://mlsanity.com/paper/2301.00802/) (2023)
- [Rethinking Image Forgery Detection via Soft Contrastive Learning and Unsupervised Clustering](https://mlsanity.com/paper/2308.09307/) (2023)
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- [K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation](https://mlsanity.com/paper/2205.08671/) (2022)
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- [Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2410.09491/) (2024)
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- [Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows](https://mlsanity.com/paper/2401.10620/) (2024)
- [Differentiable Rule Induction from Raw Sequence Inputs](https://mlsanity.com/paper/2602.13583/) (2026)
- [Mining Electronic Health Records to Investigate Effectiveness of Ensemble Deep Clustering](https://mlsanity.com/paper/2604.07085/) (2026)
- [Deep clustering with fusion autoencoder](https://mlsanity.com/paper/2201.04727/) (2022)
- [Variational Autoencoder with Disentanglement Priors for Low-Resource Task-Specific Natural Language Generation](https://mlsanity.com/paper/2202.13363/) (2022)
- [VAESim: A probabilistic approach for self-supervised prototype discovery](https://mlsanity.com/paper/2209.12279/) (2022)
- [Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows](https://mlsanity.com/paper/2305.01539/) (2023)
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## Abstract (arXiv)

We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks are performed jointly. We propose here such an approach for k-Means clustering based on a continuous reparametrization of the objective function that leads to a truly joint solution. The behavior of our approach is illustrated on various datasets showing its efficacy in learning representations for objects while clustering them.