Suppose I have a multi-class classification task. As far as I can tell, the primary metrics used for evaluating performance on this task is either to compute the confusion matrix, or the per-class f score.
However, I was wondering if we could apply cluster evaluation measures, such as the one proposed here, to serve as another metric for this multi-class classification task. If we put all datapoints with the same predicted class label into their own cluster, and compare the clusters induced by our predicted labels vs the clusters induced by the actual labels, would the resulting cluster evaluation metric also serve as a good classification metric?