When comparing clusterings, what makes a better solution?
In a new preprint, I describe homogeneity鈥損arsimony curves as a means to quantify the underlying trade-off. Think of it as an ROC curve, but for clustering validation.
arxiv.org/abs/2607.20799
In a new preprint, I describe homogeneity鈥損arsimony curves as a means to quantify the underlying trade-off. Think of it as an ROC curve, but for clustering validation.
arxiv.org/abs/2607.20799
arxiv.org External Clustering Validation by the Homogeneity-Parsimony Trade-off Scalar metrics are often used to evaluate clusterings against known classes, but they can obscure a fundamental trade-off: clusterings should be informative about class labels while avoiding... 