In this paper, Spectral Bridges, a novel clustering algorithm, is introduced. This algorithm builds upon the traditional k-means and spectral clustering frameworks by subdividing data into small Vorono\"i regions, which are subsequently merged according to a connectivity measure. Drawing inspiration from Support Vector Machine's margin concept, a non-parametric clustering approach is proposed, building an affinity margin between each pair of Vorono\"i regions. This approach is characterized by minimal hyperparameters and delineation of intricate, non-convex cluster structures. The numerical experiments underscore Spectral Bridges as a fast, robust, and versatile tool for sophisticated clustering tasks spanning diverse domains. Its efficacy extends to large-scale scenarios encompassing both real-world and synthetic datasets. The Spectral Bridge algorithm is implemented both in Python (<https://pypi.org/project/spectral-bridges>) and R <https://github.com/cambroise/spectral-bridges-Rpackage>).

翻译：本文介绍了一种新颖的聚类算法——谱桥算法。该算法在传统k均值与谱聚类框架基础上，通过将数据细分为小的Voronoi区域，并依据连通性度量进行区域合并。受支持向量机间隔概念的启发，本文提出了一种非参数化聚类方法，在每对Voronoi区域间构建亲和间隔。该方法具有超参数极少、能刻画复杂非凸聚类结构的特点。数值实验表明，谱桥算法在处理跨领域复杂聚类任务时，是一种快速、鲁棒且通用的工具。其有效性延伸至包含真实数据集与合成数据集的大规模场景。谱桥算法已在Python（<https://pypi.org/project/spectral-bridges>）与R（<https://github.com/cambroise/spectral-bridges-Rpackage>）平台实现。