Hi,
Here is the deal :
I have a 15 Million Lines and 500 Variables which makes the huge dataset.
I Want to make a behavioral segmentation.
First, i have to choose the variables that are most significant to have just the essential elements and then proceed by k-means for segmentation.
How can i choose the significant variables?
a discriminant analysis on a random sample will be usefull to keep relevant variables, start by using PROC STEPDISC.
Thank you, i'm testing it, i'll get back to you if i have any further questions
I got another issue :
I do not have a dependant variable. It's just a list of 500 variables.
Any ideas on how to do the selection?
No code, but some ideas.
If you have access to Enterprise Miner, then a lot of other techniques become available, most of which have the word "tree" in their name.
Steve Denham
Thank you very much, I'll get on it.
Hope you have sorted your problem with methods described above.
Just wondering what types of variables you have and did you also try factor analysis and MODECLUS?
I had same problem with no. of significant variables, so curious to know which technique was most useful.
Varsha,
I am going to use SteveDenham idea, it's very logical and seems that it would work.
I am still on some other tasks that take memory as well. I tried it on another laptop and works just fine.
Proc varclus to see how the variables cluster and then from a business perspective i chose the one i judged important from each cluster and some others and then i added other ones even though they didn't show much in the clustering but they are necessary for this exercise.
Hope i won't run into any trouble, in that case i'll be back to bother you guys
good day to ye !
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