So, to make along story short, how do you quantify a pattern across time, so you can cluster series that are similar. When I did the clustering, the groups with similar patterns were put together. I then took that same data and made all the means equal. Also, the series that had similar patterns were not grouped, because the means were different. The clustering grouped the series that had similar means, even though the patterns across time were different. And those series that had different patterns across time were given similar means. I then created data with patterns across time, with some series having the same pattern. Even with telling the software to standardize the data, the series that had similar means were clustered together. I started by creating several series of data with no pattern across time, but with different averages. That didn't sit right with me, so I created some data to see how it would work. I haven't used PROC Similarity, so I can't comment about the proc.Ī friend of mine told me once he was clustering a number of series according to their measurements across time. By no means am I an expert on the matter.
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