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 \textit{feature selection} and two \textit{feature extraction} based algorithms, all of which are randomized.

20 pages, 1 table, expository

References in corpus (1)

• Random Projections for $k$-means Clustering