Dec 12, 2012 · Using successive sampling, we develop an algorithm for the k-median problem that runs in O(nk) time for a wide range of values of k and is ...
One of the main contributions of this paper is a simple but powerful sampling technique that we call successive sampling that could be of independent interest.
Clustering is a fundamental problem in unsuper vised learning, and has been studied widely both as a problem of learning mixture models and.
Abstract. Clustering is a fundamental problem in unsupervised learning, and has been studied widely both as a problem of learning mixture models and as an.
Abstract. We give randomized constant-factor approximation algorithms for the k-median problem and an intimately related clustering problem.
Using successive sampling, we develop an algorithm for the k -median problem that runs in O ( nk ) time for a wide range of values of k and is guaranteed, with ...
Using successive sampling, we develop an algorithm for the k-median problem that runs in O(nk) time for a wide range of values of k and is guaranteed, with high ...
The running time depends principally on the update step at the end of the loop; it takes O(n) time per iteration of the loop, for a total running time of O(kn).
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Aug 13, 2018 · Bibliographic details on Optimal Time Bounds for Approximate Clustering.
A common approach to clustering data is to view data objects as points in a metric space, and then to optimize a natural distance-based objective such as ...