# Deep Continuous Clustering

arXiv: https://arxiv.org/abs/1803.01449 · HTML: https://mlsanity.com/paper/1803.01449/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 21 of 21)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [DeepDPM: Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2203.14309/) (2022)
- [Deep Embedded K-Means Clustering](https://mlsanity.com/paper/2109.15149/) (2021)
- [Neural Clustering based Visual Representation Learning](https://mlsanity.com/paper/2403.17409/) (2024)
- [Deep Fair Discriminative Clustering](https://mlsanity.com/paper/2105.14146/) (2021)
- [A Survey on Deep Clustering: From the Prior Perspective](https://mlsanity.com/paper/2406.19602/) (2024)
- [Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations](https://mlsanity.com/paper/2202.11917/) (2022)
- [Representation Learning for Clustering via Building Consensus](https://mlsanity.com/paper/2105.01289/) (2021)
- [Online Deterministic Annealing for Classification and Clustering](https://mlsanity.com/paper/2102.05836/) (2021)
- [cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process](https://mlsanity.com/paper/2407.11448/) (2024)
- [SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding](https://mlsanity.com/paper/2206.10670/) (2022)
- [DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Information](https://mlsanity.com/paper/2305.18492/) (2023)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning](https://mlsanity.com/paper/2602.05749/) (2026)
- [Learning Multimodal Affinities for Textual Editing in Images](https://mlsanity.com/paper/2103.10139/) (2021)
- [Snapshot Spectral Clustering -- a costless approach to deep clustering ensembles generation](https://mlsanity.com/paper/2307.08591/) (2023)
- [Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k](https://mlsanity.com/paper/2401.04741/) (2024)
- [Datacube segmentation via Deep Spectral Clustering](https://mlsanity.com/paper/2401.17695/) (2024)
- [Object Tracking in a 360o View: A Novel Perspective on Bridging the Gap to Biomedical Advancements](https://mlsanity.com/paper/2412.01119/) (2024)
- [Do Vision Transformers Need All-to-All Attention? Global Communication Through Elastic Learned Cores](https://mlsanity.com/paper/2605.12491/) (2026)

## Abstract (arXiv)

Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network produces clustered data. The presented approach does not rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear dimensionality reduction and clustering are formulated as optimization of a global continuous objective. We thus avoid discrete reconfigurations of the objective that characterize prior clustering algorithms. Experiments on datasets from multiple domains demonstrate that the presented algorithm outperforms state-of-the-art clustering schemes, including recent methods that use deep networks.