Randomized Dimensionality Reduction for k-means Clustering This paper makes further progress towards a better understanding of dimensionality reduction for kmeans clustering. Namely, we present the first provably accurate feature selection method for k-means clustering and, in addition, we present two feature extractionmethods. The first feature extractionmethod is based on random projections and it improves upon the existing results in terms of time complexity and number of features needed to be extracted. The second feature extraction method is based on fast approximate SVD factorizations and it also improves upon the existing results in terms of time complexity. The proposed algorithms are randomized and provide constant-factor approximation guarantees with respect to the optimal k-means objective value.

randomized dimensionality reduction for k-means.pdf

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