Fast Approximate ℓ-Center Clustering in High-Dimensional Spaces
We study the design of efficient approximation algorithms for the ℓ-center clustering and minimum-diameter ℓ-clustering problems in high-dimensional Euclidean and Hamming spaces. Our main tool is randomized dimension reduction. First, we present a general method of reducing the dependency of the running time of a hypothetical algorithm for the ℓ-center problem in a high-dimensional Euclidean space
