The JackStraw is a method to get honest p-values for correlations between variables and principal components derived from those variables.
There is a close relationship between PCA and clustering: K clusters gives you a signal in the top K-1 principal components, as outlined in this paper on PCA for detecting population substructure in genetic data. There is a more thorough and less sloppy discussion in this CV thread.
Does anyone know of a JackStraw equivalent that gives honest p-values for correlations between variables and cluster indicators? Preferably for an arbitrary clustering method and an arbitrary two-sample test?