FastAMI——聚类比较指标中随机性调整的蒙特卡罗方法
FastAMI -- a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
AI总结：
针对大规模数据集聚类比较中随机性调整的计算难题，提出基于蒙特卡罗的 FastAMI 方法高效近似 AMI 并扩展至 SMI，在保证比成对方法更高准确率的同时实现了大型数据集的快速信息论比较。
摘要：
聚类是机器学习的核心，随着数据可用性的增加，其应用不断扩展。然而，随着数据集的增长，对聚类进行随机性调整的比较在计算上变得困难，阻碍了无偏的真实值比较和解决方案选择。我们提出了 FastAMI，一种基于蒙特卡罗的方法，用于高效近似 Adjusted Mutual Information (AMI)，并将其扩展到 Standardized Mutual Information (SMI)。该方法使用合成数据和真实数据，与精确计算以及最近开发的基于成对排列的 AMI 变体进行了比较。与精确计算相比，我们的方法速度足够快，能够为大型数据集实现这些调整后的信息论比较，同时保持比成对方法准确得多的结果。
英文摘要：
Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data. However, as datasets grow, comparing clusterings with an adjustment for chance becomes computationally difficult, preventing unbiased ground-truth comparisons and solution selection. We propose FastAMI, a Monte Carlo-based method to efficiently approximate the Adjusted Mutual Information (AMI) and extend it to the Standardized Mutual Information (SMI). The approach is compared with the exact calculation and a recently developed variant of the AMI based on pairwise permutations, using both synthetic and real data. In contrast to the exact calculation our method is fast enough to enable these adjusted information-theoretic comparisons for large datasets while maintaining considerably more accurate results than the pairwise approach.
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