# GBC: An Efficient and Adaptive Clustering Algorithm Based on Granular-Ball

arXiv: https://arxiv.org/abs/2205.14592 · HTML: https://mlsanity.com/paper/2205.14592/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 2 of 2)

- [Border-Peeling Clustering](https://mlsanity.com/paper/1612.04869/) (2016)
- [RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density](https://mlsanity.com/paper/1611.00484/) (2016)

## Built on by (most cited first, 4 of 4)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex Data](https://mlsanity.com/paper/2410.13917/) (2024)
- [3DGBGS: 3D Granular Ball Gaussian Splatting for Compact Novel View Synthesis](https://mlsanity.com/paper/2607.26578/) (2026)
- [Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning](https://mlsanity.com/paper/2609.06016/) (2026)

## Abstract (arXiv)

Existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Inspired by adaptive process of granular-ball division and differentiation, we present a novel clustering approach that retains the speed and efficiency of K-means clustering while out-performing time-tested density clustering approaches widely used in industry today. Our simple, robust, adaptive granular-ball clustering method can efficiently recognize clusters with unknown and complex shapes without the use of extra parameters. Moreover, the proposed method provides an efficient, adaptive way to depict the world, and will promote the research and development of adaptive and efficient AI technologies, especially density computing models, and improve the efficiency of many existing clustering methods.