# How can I assess how descriptive feature vectors are?

I am assessing how good different features are for unsupervised classification of a set of objects. For each different feature I test, I have computed a feature vector that describes the object. I then want to get a metric out for how 'good' this vector is at separating the objects into their respective classes.

My current method for doing this is to use k-means to cluster the objects in feature space, and then use the Adjusted Rand Index to assess the quality of the clustering.

However, is there a better way to assess the 'goodness' of the feature vectors, perhaps using something like mutual information? One drawback with using k-means and the ARI is it provides no indication of the tightness of clustering.

If you are using R, I warmly recommend taking a look at the fpc package, by Christian Hennig, which provides almost all statistical indices described above (cluster.stats()) as well as a bootstrap procedure (clusterboot()).