# GBSK: Skeleton Clustering via Granular-ball Computing and Multi-Sampling for Large-Scale Data

arXiv: https://arxiv.org/abs/2509.23742 · HTML: https://mlsanity.com/paper/2509.23742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment](https://mlsanity.com/paper/2210.05357/) (2022)
- [Adaptive Sampled Softmax with Kernel Based Sampling](https://mlsanity.com/paper/1712.00527/) (2017)
- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection](https://mlsanity.com/paper/2408.15020/) (2024)
- [Fast Density-Peaks Clustering: Multicore-based Parallelization Approach](https://mlsanity.com/paper/2207.04649/) (2022)
- [A sampling-based approach for efficient clustering in large datasets](https://mlsanity.com/paper/2112.14793/) (2021)

## Built on by (most cited first, 1 of 1)

- [DK-GBMKKM: Dynamic Kernel-Space Granular-Ball Multiple Kernel k-Means Clustering](https://mlsanity.com/paper/2609.00647/) (2026)

## Abstract (arXiv)

To effectively handle clustering task for large-scale datasets, we propose a novel scalable skeleton clustering algorithm, namely GBSK, which leverages the granular-ball technique to capture the underlying structure of data. By multi-sampling the dataset and constructing multi-grained granular-balls, GBSK progressively uncovers a statistical "skeleton" -- a spatial abstraction that approximates the essential structure and distribution of the original data. This strategy enables GBSK to dramatically reduce computational overhead while maintaining high clustering accuracy. In addition, we introduce an adaptive version, AGBSK, with simplified parameter settings to enhance usability and facilitate deployment in real-world scenarios. Extensive experiments conducted on standard computing hardware demonstrate that GBSK achieves high efficiency and strong clustering performance on large-scale datasets, including one with up to 100 million instances across 256 dimensions. Our implementation and experimental results are available at: https://github.com/XFastDataLab/GBSK/.