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05-18-2016 10:57 AM

I have a data set with a large number of input variables, many of which are highly correlated. The variable clustering node does a nice job of reducing the number of variable and selecting a cluster representative, but I have a question about the algorithm that the documentation doesn't seem to address.

What role does the target variable play in the Variable Clustering node? Are the variables in a cluster selected just because they are similar, or do they have to have a simialr relationship to the target variabel as well?

In contrast, the Variable Selection node takes into account the strength of association between an input and the target.

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09-13-2017
02:54 PM

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Posted in reply to BrianLoe

05-18-2016 01:23 PM

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09-13-2017
02:54 PM

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Posted in reply to BrianLoe

05-18-2016 01:23 PM

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Posted in reply to WendyCzika

05-19-2016 10:10 PM

Hi,

If you believe the variance associated with each observation is 'according to' the target variable, you may consider listing the target variable at the WEIGHT statement in proc varclus.

If you believe the variance associated with each observation is 'according to' the target variable, you may consider listing the target variable at the WEIGHT statement in proc varclus.

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Posted in reply to BrianLoe

05-24-2016 09:29 AM - edited 05-24-2016 09:30 AM

I should just withdraw the question. If two variables act alike, then they would be correlated with the target in the same way as well. Since my goal was to use clustering for variable selection before constructing a regression model, variable that were aligned enough to be in the same cluster would necessarily have similar relationships with the target for regression. There was no need to consider the target variable in the the Variable Clustering node other than to withhold it from all of the clusters.

I actually got fairly strong results from regression using clustering as my method of variable selection, although a LARS node with the LASSO option proved to be the best model.

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Posted in reply to BrianLoe

09-13-2017 02:54 PM

variable that were aligned enough to be in the same cluster would necessarily have similar relationships with the target for regression

I would disagree with this statement somewhat since it really depends on the nature of the relationship. Correlation measures linear association and it is possible to have two variables have the same 'correlation' score yet have a very different relationships. A variable with a slight linear relationship which provides minor improvements in prediction could have the same correlation with the target as a variable which has a quadratic highly predictive relationship to the target value since corrrelation only measures linearity. Simpson's paradox assures us that things might not be simple even when the relationships are essentially linear. When dealing with data mining problems, the number of dimensions makes it very difficult to investigate the true nature of the relationships without spending an inordinate amount of time on investigating the same.

Cordially,

Doug