想了解怎么用CI、PSI这些指标来评判聚类好坏？这篇论文把常见外部度量讲清楚了，还给了推荐优先级。
本文回顾了外部有效性指标，重点介绍基于集合匹配的度量。推荐Centroid Index (CI)作为直观的簇级度量，结果可解释。如果需要更精细的点级度量，可选择Pair-set Index (PSI)，它提供不受簇大小影响的归一化分数。若所有点应权重相同，则聚类准确率(ACC)等集合匹配度量也适用。
How to evaluate clustering with ground truth?
External indexes can be used for cluster evaluation when ground truth is available. We review the most common external validity indexes focusing on set-matching-based measures. We recommend centroid index (CI), because it is an intuitive cluster-level measure with an explainable result. If we need a more fine-tuned, point-level measure, there are more choices. Pair-set index (PSI) provides a normalized score which is not biased by cluster sizes. If all points should matter equally, then clustering accuracy (ACC) or any other set-matching measure is suitable.