08-21-2017 10:41 PM - edited 08-22-2017 06:58 PM
I have a dataset that has 700,000 rows and various variables with mixed data-types: categorical, numeric and binary. I have read several suggestions on how to cluster categorical data but still couldn't find a solution for my problem. The common suggestions are listed below:
1) Use proc distance for the categorical variables to get a distance matrix and then use proc cluster: I can't do that as my dataset is too big to be handled by proc cluster. Another constraint is that even proc fastclus can handle a large dataset but it doesn't work with distance matrix or anything other than numeric data.
2) I don't find "creating dummy variables for the categorical variables" a good solution either as I feel the clusters would be hard to interpret.
3) Another suggestion was to use HPCLUS but HPCLUS can either use only categorical variables or only numeric interval variables to perform clustering but it does not perform clustering for mixed levels of input variables
Is there any other solution/proc available in SAS that could perform clustering on a large dataset with mixed variable types?
08-21-2017 11:00 PM
How many variables do you have? 700k is a lot but I would have assumed SAS could handle it. Can you change your memory settings to allow it to be processed?
08-21-2017 11:29 PM
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