Dimensionality Reduction for k-means Clustering

Abstract

We present a study on how to effectively reduce the dimensions of the k-means clustering problem, so that provably accurate approximations are obtained. Four algorithms are presented, two feature selection and two feature extraction based algorithms, all of which are randomized.

Tasks

Reproductions