# LGBQPC: Local Granular-Ball Quality Peaks Clustering

arXiv: https://arxiv.org/abs/2505.11359 · HTML: https://mlsanity.com/paper/2505.11359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 16 May 2025.

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

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [An Efficient and Adaptive Granular-ball Generation Method in Classification Problem](https://mlsanity.com/paper/2201.04343/) (2022)
- [Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering](https://mlsanity.com/paper/1802.10549/) (2018)
- [GBG++: A Fast and Stable Granular Ball Generation Method for Classification](https://mlsanity.com/paper/2305.18450/) (2023)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Generation of Granular-Balls for Clustering Based on the Principle of Justifiable Granularity](https://mlsanity.com/paper/2405.06904/) (2024)
- [Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls](https://mlsanity.com/paper/2501.02975/) (2025)

## Abstract (arXiv)

The density peaks clustering (DPC) algorithm has attracted considerable attention for its ability to detect arbitrarily shaped clusters based on a simple yet effective assumption. Recent advancements integrating granular-ball (GB) computing with DPC have led to the GB-based DPC (GBDPC) algorithm, which improves computational efficiency. However, GBDPC demonstrates limitations when handling complex clustering tasks, particularly those involving data with complex manifold structures or non-uniform density distributions. To overcome these challenges, this paper proposes the local GB quality peaks clustering (LGBQPC) algorithm, which offers comprehensive improvements to GBDPC in both GB generation and clustering processes based on the principle of justifiable granularity (POJG). Firstly, an improved GB generation method, termed GB-POJG+, is developed, which systematically refines the original GB-POJG in four key aspects: the objective function, termination criterion for GB division, definition of abnormal GB, and granularity level adaptation strategy. GB-POJG+ simplifies parameter configuration by requiring only a single penalty coefficient and ensures high-quality GB generation while maintaining the number of generated GBs within an acceptable range. In the clustering phase, two key innovations are introduced based on the GB k-nearest neighbor graph: relative GB quality for density estimation and geodesic distance for GB distance metric. These modifications substantially improve the performance of GBDPC on datasets with complex manifold structures or non-uniform density distributions. Extensive numerical experiments on 40 benchmark datasets, including both synthetic and publicly available datasets, validate the superior performance of the proposed LGBQPC algorithm.