3 • We train a "meta-classifier" that predicts the true rank (or accuracy) of each clustering based on the feature vector of the clustering.
for each task (Binary, Multi5, and Multi10) and each clustering method (sIB and IDT), a meta-classifier is learned thus: • We obtain 10 clusterings (α = 0.1, 0.2, .