How can i decide on which features to use for clustering? I am clustering on a dataset where each row is a customer and each column is a feature. I have 200 features, this seems like alot for clustering. I plan to experiment with a variety of clustering models e.g. k-means since all my data is numerical. H
How can i reduce/select the features ?
I am only familiar with SelectKbest, etc as these are used for predictive modelling. But here i have NO target variable. Note i plan to use python.
Good points by shepan6. It is also worth to mention about feature extraction techniques to lower the number of  features. You can use common feature extraction techniques such as PCA or eigen projections to transform the data into a lower dimensions. After lowering the number of features like this you can appply  techniques such as clustering