# GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data

arXiv: https://arxiv.org/abs/2410.13917 · HTML: https://mlsanity.com/paper/2410.13917/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 8 of 8)

- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [A Unified Granular-ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set](https://mlsanity.com/paper/2201.03349/) (2022)
- [GBSVM: Granular-ball Support Vector Machine](https://mlsanity.com/paper/2210.03120/) (2022)
- [GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball](https://mlsanity.com/paper/2205.14592/) (2022)
- [Graph-based Representation for Image based on Granular-ball](https://mlsanity.com/paper/2303.02388/) (2023)
- [GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems](https://mlsanity.com/paper/2303.12807/) (2023)

## Built on by (most cited first, 2 of 2)

- [Granular-Ball-Induced Multiple Kernel K-Means](https://mlsanity.com/paper/2506.18637/) (2025)
- [Granular Ball K-Class Twin Support Vector Classifier](https://mlsanity.com/paper/2412.05438/) (2024)

## Abstract (arXiv)

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in human brain, resulting in those methods' bad performance in efficiency, generalization ability and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering (GBCT) via granular-ball computing. Firstly, GBCT generates a smaller number of granular-balls to represent the original data, and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust; besides, as granular-balls can fit various complex data, GBCT performs much better in non-spherical data sets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also used to improve other traditional methods.