By no means am I an expert on the matter. I haven't used PROC Similarity, so I can't comment about the proc.
A friend of mine told me once he was clustering a number of series according to their measurements across time. That didn't sit right with me, so I created some data to see how it would work.
I started by creating several series of data with no pattern across time, but with different averages. Even with telling the software to standardize the data, the series that had similar means were clustered together.
I then created data with patterns across time, with some series having the same pattern. And those series that had different patterns across time were given similar means. The clustering grouped the series that had similar means, even though the patterns across time were different. Also, the series that had similar patterns were not grouped, because the means were different.
I then took that same data and made all the means equal. When I did the clustering, the groups with similar patterns were put together.
So, to make along story short, how do you quantify a pattern across time, so you can cluster series that are similar. That's a hard question. The patterns have to be more obvious than differences in series averages, or other things you don't want to cluster on.
I'm sure there's more to this, but I'm not educated enough on the subject to comment futher.
Message was edited by: jonathan@jmp