document: An objective evaluation criterion for clustering We propose and test an objective criterion for evaluation of clustering performance : How well does a clustering algorithm run on unlabeled data aid a classification algorithm ? The accuracy is quantified using the PAC-MDL bound ( 3 ) in a semisupervised setting . Clustering algorithms which naturally separate the data according to ( hidden ) labels with a small number of clusters perform well . A simple extension of the argument leads to an objective model selection method . Experimental results on text analysis datasets demonstrate that this approach empirically results in very competitive bounds on test set performance on natural datasets .
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