归一化聚类准确率：一种非对称的外部聚类有效性度量
Normalised clustering accuracy: An asymmetric external cluster validity measure
AI总结：
针对现有聚类外部有效性度量在识别最坏情况和可解释性上的不足，提出一种归一化、单调、尺度不变且校正簇不平衡的最优集合匹配准确率变体作为新度量。
摘要：
不存在，也永远不会存在，单一最好的聚类算法。尽管如此，我们仍然希望能够区分在特定任务类型上表现良好的方法和那些系统性表现不佳的方法。聚类算法传统上使用内部或外部有效性度量进行评估。内部度量量化所获得划分的不同方面，例如簇紧凑性或点可分性的平均程度。然而，其有效性值得怀疑，因为它们认可的聚类有时可能毫无意义。另一方面，外部度量将算法的输出与专家提供的固定真实分组进行比较。在本文中，我们认为常用的经典划分相似度分数，如归一化互信息、Fowlkes-Mallows或调整兰德指数，缺少一些理想的属性。特别是，它们不能正确识别最坏情况，也不易于解释。因此，在不同基准数据集上评估聚类算法可能很困难。为了解决这些问题，我们提出并分析了一种新的度量：最优集合匹配准确率的一个版本，该度量是归一化的，相对于某种相似性关系是单调的，尺度不变的，并针对簇大小的不平衡性进行了校正（但既不对称也不针对机会进行调整）。
英文摘要：
There is no, nor will there ever be, single best clustering algorithm. Nevertheless, we would still like to be able to distinguish between methods that work well on certain task types and those that systematically underperform. Clustering algorithms are traditionally evaluated using either internal or external validity measures. Internal measures quantify different aspects of the obtained partitions, e.g., the average degree of cluster compactness or point separability. However, their validity is questionable because the clusterings they endorse can sometimes be meaningless. External measures, on the other hand, compare the algorithms' outputs to fixed ground truth groupings provided by experts. In this paper, we argue that the commonly used classical partition similarity scores, such as the normalised mutual information, Fowlkes-Mallows, or adjusted Rand index, miss some desirable properties. In particular, they do not identify worst-case scenarios correctly, nor are they easily interpretable. As a consequence, the evaluation of clustering algorithms on diverse benchmark datasets can be difficult. To remedy these issues, we propose and analyse a new measure: a version of the optimal set-matching accuracy, which is normalised, monotonic with respect to some similarity relation, scale-invariant, and corrected for the imbalancedness of cluster sizes (but neither symmetric nor adjusted for chance).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
