Add to ORKGWe present the rst constant-factor approximation algorithm for the metric k-median problem. The k-median problem is one of the most well-studied clustering problems, i.e., those problems in which the aim is to partition a given set of points into clusters so that the points within a cluster are relatively close with respect to some measure. For the metric k-median problem, we are given n points in a metric space. We select k of these to be cluster centers, and then assign each point to its closest selected center. If point j is assigned to a center i, the cost incurred is proportional to the distance between i and j. The goal is to select the k centers that minimize the sum of the assignment costs. We give a 6 2/3-approximation algorith...
Abstract. In this paper we present a novel analysis of a random sampling approach for three clusteri...
We present a novel analysis of a random sampling approach for four clustering problems in metric spa...
We consider a generalization of k-median and k-center, called the ordered k-median problem. In this ...
AbstractWe present the first constant-factor approximation algorithm for the metric k-median problem...
Given a set of n points and their pairwise distances, the goal of clustering is to partition the po...
We consider two closely related fundamental clustering problems in this paper. In the min-sum k-clus...
In discrete k-center and k-median clustering, we are given a set of points P in a metric space M, an...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
In discrete κ-center and κ-median clustering, we are given a set of points P in a metric space M, an...
We study approximation algorithms for k-median clustering. We obtain small coresets for k-median clu...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
In the aversion k-clustering problem, given a metric space, we want to cluster the points into k clu...
In the k-median problem we are given a set S of n points in a metric space and a positive integer k....
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
International audienceWe consider the classic Facility Location, k-Median, and k-Means problems in m...
Abstract. In this paper we present a novel analysis of a random sampling approach for three clusteri...
We present a novel analysis of a random sampling approach for four clustering problems in metric spa...
We consider a generalization of k-median and k-center, called the ordered k-median problem. In this ...
AbstractWe present the first constant-factor approximation algorithm for the metric k-median problem...
Given a set of n points and their pairwise distances, the goal of clustering is to partition the po...
We consider two closely related fundamental clustering problems in this paper. In the min-sum k-clus...
In discrete k-center and k-median clustering, we are given a set of points P in a metric space M, an...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
In discrete κ-center and κ-median clustering, we are given a set of points P in a metric space M, an...
We study approximation algorithms for k-median clustering. We obtain small coresets for k-median clu...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
In the aversion k-clustering problem, given a metric space, we want to cluster the points into k clu...
In the k-median problem we are given a set S of n points in a metric space and a positive integer k....
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
International audienceWe consider the classic Facility Location, k-Median, and k-Means problems in m...
Abstract. In this paper we present a novel analysis of a random sampling approach for three clusteri...
We present a novel analysis of a random sampling approach for four clustering problems in metric spa...
We consider a generalization of k-median and k-center, called the ordered k-median problem. In this ...