I want to use a clustering algorithm to a dataframe that contains a lot of features (32 columns).

A part of the features are encoded using one hot encoder.

I want to use PCA ( Principal Component analysis ) to reduce the dimension and make the machine learning process easier.

Is it possible to use the PCA just for some columns of the data frame and keep the other columns as they are then use machine learning model.

Or it is obligatory to use PCA for all the dataframe before clustering.

CodePudding user response：

I guess there should be no issue with doing what you describe.

What this does, effectively, is merge some of the objects' features into fewer ones, but then using other, non-merged ones in addition to the merged ones. I don't know what effect that would have on the outcome; it might be good to run a correlation to see whether the unmerged features add anything to the PCA-merged ones. You might find that they basically duplicate what is there already.

Since clustering is an exploratory method, you can basically do whatever you want. It is of course advisable to have a reason for doing so, as it otherwise ends up as simply trial-and-error, and if you find a result, you won't be able to describe why you got there. It is possible (or even likely for some data sets) that there are multiple ways to cluster them, so you should make decisions based on what you know about the data already, so they can be justified in those terms.

Running random trial-and-error clustering until you find a structure makes it a bit difficult to come up with a good explanation why that structure is valid.