A Simple Approach to Sparse Clustering

02/23/2016
by   Ery Arias-Castro, et al.
0

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, subsequently improved in the form of the Sparse K-means method of Witten and Tibshirani, a natural and simpler hill-climbing approach is introduced. The new method is shown to be competitive with these two methods and others.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset