We introduce CoHiRF (Consensus Hierarchical Random Features), a hierarchical consensus framework that enables existing clustering methods to operate beyond their usual computational and memory limits. CoHiRF is a meta-algorithm that operates exclusively on the label assignments produced by a base clustering method, without modifying its objective function, optimization procedure, or geometric assumptions. It repeatedly applies the base method to multiple low-dimensional feature views or stochastic realizations, enforces agreement through consensus, and progressively reduces the problem size via representative-based contraction. Across a diverse set of synthetic and real-world experiments involving centroid-based, kernel-based, density-based, and graph-based methods, we show that CoHiRF can improve robustness to high-dimensional noise, enhance stability under stochastic variability, and enable scalability to regimes where the base method alone is infeasible. We also provide an empirical characterization of when hierarchical consensus is beneficial, highlighting the role of reproducible label relations and their compatibility with representative-based contraction. Beyond flat partitions, CoHiRF produces an explicit Cluster Fusion Hierarchy, offering a multi-resolution and interpretable view of the clustering structure. Together, these results position hierarchical consensus as a practical and flexible tool for large-scale clustering, extending the applicability of existing methods without altering their underlying behavior.

翻译：本文提出CoHiRF（共识分层随机特征），一种分层共识框架，使现有聚类方法能够突破常规计算与内存限制运行。CoHiRF是一种元算法，仅基于底层聚类方法生成的标签分配进行操作，无需修改其目标函数、优化过程或几何假设。该框架通过以下步骤实现：对多个低维特征视图或随机实现重复应用底层方法，通过共识机制强制达成一致，并借助基于代表点的收缩策略逐步缩减问题规模。在涵盖基于质心、基于核函数、基于密度和基于图方法的合成与真实数据集实验中，我们证明CoHiRF能够提升对高维噪声的鲁棒性、增强随机变异下的稳定性，并实现底层方法单独运行时不可行的规模扩展。我们通过实证研究明确了分层共识的适用条件，强调可复现标签关系及其与基于代表点收缩策略的兼容性所起的关键作用。除平面划分外，CoHiRF还生成显式的聚类融合层次结构，为聚类结构提供多分辨率且可解释的视图。这些成果共同将分层共识定位为大规模聚类的实用灵活工具，在保持现有方法内在特性的同时显著扩展其适用范围。