# Clustering and variable selection

Let's say I'm trying to cluster $n$ points in $\mathbb{R}^p$, and I know in advance that only $s$ many of these $p$ dimensions determine the differences between the clusters. Of course, I don't know which of these dimensions are the important ones.

All in all, I have an unsupervised learning problem (clustering) and I'm trying to do variable selection while also trying to determine the clusters. Are there any well known algorithms for this? Or better, any theoretical results?

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