Clustering algorithms are an essential part of the unsupervised data science ecosystem, and extrinsic evaluation of clustering algorithms requires a method for comparing the detected clustering to a ground truth clustering. In a general setting, the detected and ground truth clusterings may have outliers (objects belonging to no cluster), overlapping clusters (objects may belong to more than one cluster), or both, but methods for comparing these clusterings are currently undeveloped. In this note, we define a pragmatic similarity measure for comparing clusterings with overlaps and outliers, show that it has several desirable properties, and experimentally confirm that it is not subject to several common biases afflicting other clustering comparison measures.

翻译：聚类算法是无监督数据科学生态系统的核心组成部分，对聚类算法的外部评估需要一种将检测到的聚类结果与真实聚类进行比较的方法。在一般设定下，检测到的聚类与真实聚类可能包含离群点（不属于任何簇的对象）、重叠簇（对象可能属于多个簇）或两者兼有，但目前尚缺乏比较此类聚类的方法。本文定义了一种实用的相似性度量，用于比较包含重叠与离群点的聚类，证明了其具备多项理想性质，并通过实验验证该度量不受其他聚类比较方法中常见的多种偏差影响。