# Parameter-Free Clustering via Self-Supervised Consensus Maximization (Extended Version)

arXiv: https://arxiv.org/abs/2511.09211 · HTML: https://mlsanity.com/paper/2511.09211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 12 Nov 2025.

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

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Efficient Parameter-free Clustering Using First Neighbor Relations](https://mlsanity.com/paper/1902.11266/) (2019)
- [Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision](https://mlsanity.com/paper/2109.08203/) (2021)
- [DeepDPM: Deep Clustering With an Unknown Number of Clusters](https://mlsanity.com/paper/2203.14309/) (2022)
- [Stop using the elbow criterion for k-means and how to choose the number of clusters instead](https://mlsanity.com/paper/2212.12189/) (2022)
- [LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering](https://mlsanity.com/paper/2405.11801/) (2024)
- [Learning Hierarchical Graph Neural Networks for Image Clustering](https://mlsanity.com/paper/2107.01319/) (2021)
- [Reinforcement Graph Clustering with Unknown Cluster Number](https://mlsanity.com/paper/2308.06827/) (2023)
- [DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System](https://mlsanity.com/paper/2408.08231/) (2024)
- [Asynchronous Federated Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/2412.20341/) (2024)
- [Deep Contrastive Graph Learning with Clustering-Oriented Guidance](https://mlsanity.com/paper/2402.16012/) (2024)
- [Another Way to the Top: Exploit Contextual Clustering in Learned Image Coding](https://mlsanity.com/paper/2401.11615/) (2024)
- [Border-Peeling Clustering](https://mlsanity.com/paper/1612.04869/) (2016)
- [Multi-level Cross-modal Alignment for Image Clustering](https://mlsanity.com/paper/2401.11740/) (2024)
- [Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation](https://mlsanity.com/paper/2506.03521/) (2025)

## Abstract (arXiv)

Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Consensus Maximization, named SCMax. Our framework performs hierarchical agglomerative clustering and cluster evaluation in a single, integrated process. At each step of agglomeration, it creates a new, structure-aware data representation through a self-supervised learning task guided by the current clustering structure. We then introduce a nearest neighbor consensus score, which measures the agreement between the nearest neighbor-based merge decisions suggested by the original representation and the self-supervised one. The moment at which consensus maximization occurs can serve as a criterion for determining the optimal number of clusters. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing clustering approaches designed for scenarios with an unknown number of clusters.