The general strategy is this: we use the cluster assignments to label examples. Each example will be given a label of the cluster it belongs to, and these can be treated as class labels. Once we have a labeled set of examples, we run a supervised learning algorithm on the example set to generate a classifier for each class/cluster. We can then inspect the classifier descriptions to get a (hopefully) intelligible and concise description of the corresponding cluster. The important thing to note is that these will be differential descriptions: for each cluster, what differentiates it from the
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