App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 4

Newton-raphson optimization-driven adaptive density peak clustering

Wei Xiuxi1a
Li Kang1b
Huang Huajuan1a
Zhou Yongquan1a,1c
Pang Qiuben2
1. a. School of Artificial Intelligence, b. School of Physics and Electronic Information, c. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Guangxi Minzu University, Nanning 530006, China
2. Dept. of Information center, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0281

Abstract

To address the challenges of density peak clustering (DPC) , including manual setting of cutoff distance parameter, sensitivity to noise, and limited adaptability to non-uniform data distributions, this study designs a Newton-Raphson optimization-driven adaptive density peaks clustering method (NRO-ADPC) . This method establishes an adaptive parameter optimization mechanism based on Newton-Raphson optimization algorithm to automatically determine cutoff distance through gradient-guided second-order optimization, eliminating manual parameter tuning dependency. This study constructs a multi-objective optimization function integrating density continuity, cluster separation, and peak distinctness, combined with adaptive weighting mechanism to enhance algorithm robustness against noise. The study adopts a hybrid density estimation strategy combining fixed and adaptive bandwidth to adapt to local distribution variations while maintaining global consistency. Experiments on 5 synthetic and 5 real-world datasets demonstrate that NRO-ADPC achieves over 99% average accuracy on synthetic datasets and improves accuracy by over 20% compared to traditional DPC on real-world datasets. Wilcoxon signed-rank test shows that NRO-ADPC achieves statistically significant advantages over comparison algorithms including MDPC+ in ACC and NMI metrics (p<0.05) , exhibiting superior performance when processing complex data with high noise, multi-scale features, and non-uniform distributions.

Foundation Support

国家自然科学基金资助项目(62266007)
广西自然科学基金项目(2021GXNSFAA220068)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0281
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 4

Publish History

[2025-12-11] Accepted Paper

Cite This Article

韦修喜, 李康, 黄华娟, 等. 牛顿-拉夫逊优化驱动的自适应密度峰值聚类 [J]. 计算机应用研究, 2026, 43 (4). (2025-12-11). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0281. (Wei Xiuxi, Li Kang, Huang Huajuan, et al. Newton-raphson optimization-driven adaptive density peak clustering [J]. Application Research of Computers, 2026, 43 (4). (2025-12-11). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0281. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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