# Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity

arXiv: https://arxiv.org/abs/2405.06904 · HTML: https://mlsanity.com/paper/2405.06904/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 70 references that are in the map.
- First posted on arXiv 11 May 2024.

## Builds on (most cited first, 6 of 6)

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond](https://mlsanity.com/paper/1710.03113/) (2017)
- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)
- [GBG++: A Fast and Stable Granular Ball Generation Method for Classification](https://mlsanity.com/paper/2305.18450/) (2023)
- [Divide-and-conquer based Large-Scale Spectral Clustering](https://mlsanity.com/paper/2104.15042/) (2021)
- [Aggregation of Classifiers: A Justifiable Information Granularity Approach](https://mlsanity.com/paper/1703.05411/) (2017)

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

- [MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering](https://mlsanity.com/paper/2605.08759/) (2026)
- [A Boundary-Aware Non-parametric Granular-Ball Classifier Based on Minimum Description Length](https://mlsanity.com/paper/2605.11406/) (2026)
- [LGBQPC: Local Granular-Ball Quality Peaks Clustering](https://mlsanity.com/paper/2505.11359/) (2025)
- [Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls](https://mlsanity.com/paper/2607.21381/) (2026)

## Abstract (arXiv)

Efficient and robust data clustering remains a challenging task in the field of data analysis. Recent efforts have explored the integration of granular-ball (GB) computing with clustering algorithms to address this challenge, yielding promising results. However, existing methods for generating GBs often rely on single indicators to measure GB quality and employ threshold-based or greedy strategies, potentially leading to GBs that do not accurately capture the underlying data distribution. To address these limitations, this article introduces a novel GB generation method. The originality of this method lies in leveraging the principle of justifiable granularity to measure the quality of a GB for clustering tasks. To be precise, we define the coverage and specificity of a GB and introduce a comprehensive measure for assessing GB quality. Utilizing this quality measure, the method incorporates a binary tree pruning-based strategy and an anomaly detection method to determine the best combination of sub-GBs for each GB and identify abnormal GBs, respectively. Compared to previous GB generation methods, the new method maximizes the overall quality of generated GBs while ensuring alignment with the data distribution, thereby enhancing the rationality of the generated GBs. Experimental results obtained from both synthetic and publicly available datasets underscore the effectiveness of the proposed GB generation method, showcasing improvements in clustering accuracy and normalized mutual information.