One standard way to evaluate the quality of such techniques is to look at the distribution of correct clusterings versus incorrect clusterings.

This can be quantified by looking at the [precision, recall and quality of the clusterings][1]... but assumes that you have a "ground truth" (i.e. that you know the real cluster the data point belongs to.

Precision: $p = \frac{TP}{TP + FP}$, the percentage of positives that are true positives (and not mis-classified negatives). 

Recall: $r = \frac{TP}{TP + FN}$, the percentage of true positives that were correctly identified.

Quality (AKA accuracy) = $q = \frac{TP + TN}{TP + TN + FP + FN}$, the percentage of correctly classified items from all classified items.

Here $TP$ is the number of true positives, $TN$ the number of true negatives, $FP$ the number of false positives, $FN$ is the number of false negatives.

If you have many clusters and need to evaluate their interaction, then [a confusion matrix may be more useful.][2]


  [1]: https://en.wikipedia.org/wiki/Precision_and_recall
  [2]: http://en.wikipedia.org/wiki/Confusion_matrix