  We present a nonparametric method for selecting informative features in
high-dimensional clustering problems. We start with a screening step that uses
a test for multimodality. Then we apply kernel density estimation and mode
clustering to the selected features. The output of the method consists of a
list of relevant features, and cluster assignments. We provide explicit bounds
on the error rate of the resulting clustering. In addition, we provide the
first error bounds on mode based clustering.
