An objective evaluation criterion for clustering

@inproceedings{Banerjee2004AnOE,
  title={An objective evaluation criterion for clustering},
  author={Arindam Banerjee and John Langford},
  booktitle={KDD},
  year={2004}
}
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… CONTINUE READING
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