There are many many measures that can be used on labeled data.
For example, if you run k-means wiht $k=3$ on the mouse data set:
you get the following evaluation result (using ELKI):
Clearly, it did not work very well. If you know this toy data set, k-means just doesn't work well on it, because the clusters have too different size.
These are external evaluation measures. They work well if the labels correspond to clusters. If you are using classification data, the labels may not at all correspond to clusters; but some classes may form one big cluster, or a class may split into multiple clusters. There may also be outliers. They work well on synthetic data, but real data just never has such labels already.
So while such measures are a nice thing for experimenting, they have big issues... IMHO, their results can be totally misleading. A clustering algorithm that works perfectly well may score really bad on such a measure, if the labels do not correspond to the data clustering structure.
Clustering just is not classification. It's rather orthogonal.