Clustering High Dimensional Dynamic Data Streams
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Proceedings of the 34th International Conference on Machine Learning, PMLR 70:576585, 2017.
Abstract
We present data streaming algorithms for the $k$median problem in highdimensional dynamic geometric data streams, i.e. streams allowing both insertions and deletions of points from a discrete Euclidean space $\{1, 2, \ldots \Delta\}^d$. Our algorithms use $k \epsilon^{2} \mathrm{poly}(d \log \Delta)$ space/time and maintain with high probability a small weighted set of points (a coreset) such that for every set of $k$ centers the cost of the coreset $(1+\epsilon)$approximates the cost of the streamed point set. We also provide algorithms that guarantee only positive weights in the coreset with additional logarithmic factors in the space and time complexities. We can use this positivelyweighted coreset to compute a $(1+\epsilon)$approximation for the $k$median problem by any efficient offline $k$median algorithm. All previous algorithms for computing a $(1+\epsilon)$approximation for the $k$median problem over dynamic data streams required space and time exponential in $d$. Our algorithms can be generalized to metric spaces of bounded doubling dimension.
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