What about using silhouette graphs produced in R? Here is some background/links about the R package:
First you have to run some clustering algorithm (k-means for example). Anything that will create a clustering object. Below is some code using the Iris data. First the clustering algorithm is run. You have to choose the number of clusters. Then the silhouette plot is run. The algorithm considers every point that it put into a cluster and the distance to the centroid of that cluster. It assigns a value of between -1 and 1.
$s_x$ =1 would mean x has distance 0 to all other points in its cluster
$s_x$ > 0 means x is closer to points in its cluster than to other clusters
$s_x$ < 0 means x is closer to some other cluster than the one it is in
It averages over the entire set and creates a silhouette for each cluster. Now you can compare the goodness of each cluster versus every other cluster. Clusters close to 1 are very tightly fit. You can try different number of clusters and compare how the silhouettes come out to see if maybe a different number of clusters is appropriate by graphing average silhouette length versus number of clusters. Below is the code and the silhouette for R's Iris data. Let me know if this helps.
Iris_KM3 = kmeans(iris[,1:4],3)