This paper introduces the Stable Matching Based Pairing (SMBP) algorithm, a high-performance external validity index for clustering evaluation in large-scale datasets with a large number of clusters. SMBP leverages the stable matching framework to pair clusters across different clustering methods, significantly reducing computational complexity to $O(N^2)$, compared to traditional Maximum Weighted Matching (MWM) with $O(N^3)$ complexity. Through comprehensive evaluations on real-world and synthetic datasets, SMBP demonstrates comparable accuracy to MWM and superior computational efficiency. It is particularly effective for balanced, unbalanced, and large-scale datasets with a large number of clusters, making it a scalable and practical solution for modern clustering tasks. Additionally, SMBP is easily implementable within machine learning frameworks like PyTorch and TensorFlow, offering a robust tool for big data applications. The algorithm is validated through extensive experiments, showcasing its potential as a powerful alternative to existing methods such as Maximum Match Measure (MMM) and Centroid Ratio (CR).

本文解决了大规模数据集下多个聚类方法的聚类评价问题，提出了一种基于稳定匹配的配对（SMBP）算法，通过稳定匹配框架实现不同聚类方法间的聚类配对，计算复杂度降低至O(N²)。SMBP在真实和合成数据集上的评估显示其与传统最大加权匹配（MWM）在准确度上可比，但在计算效率上更具优势，特别适用于大规模、平衡及不平衡的数据集。

适用于大规模聚类的高性能外部有效性指标