The misclassification error distance and the adjusted Rand index are two of the most common criteria used to evaluate the performance of clustering algorithms. This paper provides an in‐depth comparison of the two criteria, with the aim of better understand exactly what they measure, their properties and their differences. Starting from their population origins, the investigation includes many data analysis examples and the study of particular cases in great detail. An exhaustive simulation study provides insight into the criteria distributions and reveals some previous misconceptions.

中文翻译：

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外部聚类评估的误分类误差距离和调整后的 Rand 指数的特写比较


误分类误差距离和调整后的兰德指数是用于评估聚类算法性能的两个最常见的标准。本文对这两个标准进行了深入比较，旨在更好地理解它们的测量内容、特性和差异。调查从人群起源出发，包含大量的数据分析实例和具体案例的细致研究。详尽的模拟研究提供了对标准分布的深入了解，并揭示了以前的一些误解。