A Simple Approach to Sparse Clustering

We consider the problem of sparse clustering, where it is assumed that only a subset of the features are useful for clustering purposes. In the framework of the COSA method of Friedman and Meulman (2004), subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani (2010), we propose a very natural and simpler hill-climbing approach that is competitive with these two methods.