Segmentation using cluster analysis in SPSS

Some users report getting insight from silhouette plots. You can read about them here http://en.wikipedia.org/wiki/Silhouette_(clustering).

They are available in Statistics via the STATS CLUS SIL available from the SPSS Community website (www.ibm.com/developerworks/spssdevcentral) and require the Python Essentials available through that site or with your Statistics installation materials (automatically installed with Statistics V22 or later)

In my opinion, the quality of your cluster solution is inherently subjective. There's no right or wrong but rather "makes sense and is actionable for us" or "doesn't make sense, is not actionable".

Now first, what you should know is that all three clustering algorithms are affected by the order of your cases. This really raises questions about the soundness of the procedure altogether. But given that there's randomness in the result anyway, you may perhaps just as well capitalize on it as long as you're transparent about what you did and your reasoning behind it.

You can run the same clustering syntax repeatedly (say 10 times) and randomly reorder cases between runs. You can automate this with Python, an example that comes close to this is Regression over Many Dependent Variables. Now you'll get 10 different solutions, compare their quality and choose what you find best.

My personal opinion is that two pieces of information matter:

• 1 How are the frequencies of cases over clusters? You usually don't want very small or very large clusters.
• 2 Can you describe what the clusters mean? For this you may run means plots of the input variables with the cluster variable.

if these results make sense (they're explicable) and they're actionable, I'd say you have a good solution.