A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers

ArXi:2602.14855v2 Announce Type: replace 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 than one cluster), or both, but methods for comparing these clusterings are currently undeveloped.