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The reverse greedy algorithm for the metric kmedian problem
 Information Processing Letters
"... The Reverse Greedy algorithm (RGreedy) for the kmedian problem works as follows. It starts by placing facilities on all nodes. At each step, it removes a facility to minimize the total distance to the remaining facilities. It stops when k facilities remain. We prove that, if the distance function i ..."
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Cited by 7 (0 self)
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The Reverse Greedy algorithm (RGreedy) for the kmedian problem works as follows. It starts by placing facilities on all nodes. At each step, it removes a facility to minimize the total distance to the remaining facilities. It stops when k facilities remain. We prove that, if the distance function
Inapproximability of Materialized View Selection Problem and Nonmetric Kmedians Problem
"... Materialized view selection problem is an important problem in data warehouse research. A lowcost way to answer a query is to precompute (materialize) the result of that query to create a view. With a given set of queries to answer and limited resources, it is an NPhard problem to select a subse ..."
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know that it is impossible to have an algorithm with approximation factor less than n (the number of views). On the other hand, we have reduced the view selection problem to the nonmetric kmedians problem and studied the similarities and differences between the two problems. We found
A constantfactor approximation algorithm for the kmedian problem
 In Proceedings of the 31st Annual ACM Symposium on Theory of Computing
, 1999
"... We present the first constantfactor approximation algorithm for the metric kmedian problem. The kmedian problem is one of the most wellstudied 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 re ..."
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Cited by 252 (12 self)
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We present the first constantfactor approximation algorithm for the metric kmedian problem. The kmedian problem is one of the most wellstudied 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
Approximation algorithms for metric facility location and kmedian problems using the . . .
"... ..."
Approximation schemes for Euclidean kMedians And Related Problems
 In Proc. 30th Annu. ACM Sympos. Theory Comput
, 1998
"... In the kmedian problem we are given a set S of n points in a metric space and a positive integer k. We desire to locate k medians in space, such that the sum of the distances from each of the points of S to the nearest median is minimized. This paper gives an approximation scheme for the plane that ..."
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Cited by 142 (3 self)
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In the kmedian problem we are given a set S of n points in a metric space and a positive integer k. We desire to locate k medians in space, such that the sum of the distances from each of the points of S to the nearest median is minimized. This paper gives an approximation scheme for the plane
Facility Location: the
"... Introduction In the class of facility location problems, we are given a set of n locations, each having a demand for a certain service. And the goal is to determine a subset of locations where to place facilities for the service. In this lecture, we study the kmedian problem. We select a set of at ..."
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in which the cost form a metric. Definition 1 (Metric kmedian Problem) The metric kmedian problem is defined as follows ffl Input: 1. n points(locations) 2. point i has
Improved Combinatorial Algorithms for the Facility Location and kMedian Problems
 In Proceedings of the 40th Annual IEEE Symposium on Foundations of Computer Science
, 1999
"... We present improved combinatorial approximation algorithms for the uncapacitated facility location and kmedian problems. Two central ideas in most of our results are cost scaling and greedy improvement. We present a simple greedy local search algorithm which achieves an approximation ratio of 2:414 ..."
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Cited by 227 (11 self)
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We present improved combinatorial approximation algorithms for the uncapacitated facility location and kmedian problems. Two central ideas in most of our results are cost scaling and greedy improvement. We present a simple greedy local search algorithm which achieves an approximation ratio of 2
Continuous Weber and kMedian Problems
, 2000
"... We give the first exact algorithmic study of facility location problems that deal with finding a median for a continuum of demand points. In particular, we consider versions of the "continuous kmedian (Weber) problem" where the goal is to select one or more center points that minimize the ..."
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Cited by 16 (2 self)
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We give the first exact algorithmic study of facility location problems that deal with finding a median for a continuum of demand points. In particular, we consider versions of the "continuous kmedian (Weber) problem" where the goal is to select one or more center points that minimize
On kmedian clustering in high dimensions
 In: Proceedings of the 17th Annual ACMSIAM Symposium on Discrete Algorithms (SODA ’06), Society for Industrial and Applied Mathematics
, 2006
"... We study approximation algorithms for kmedian clustering. We obtain small coresets for kmedian clustering in metric spaces as well as in Euclidean spaces. Specifically, in IR d, those coresets are of size with only polynomial dependency on d. This leads to a (1 + ε)approximation algorithm for km ..."
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Cited by 26 (0 self)
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We study approximation algorithms for kmedian clustering. We obtain small coresets for kmedian clustering in metric spaces as well as in Euclidean spaces. Specifically, in IR d, those coresets are of size with only polynomial dependency on d. This leads to a (1 + ε)approximation algorithm for kmedian
Mtree: An Efficient Access Method for Similarity Search in Metric Spaces
, 1997
"... A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion o ..."
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Cited by 652 (38 self)
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A new access meth d, called Mtree, is proposed to organize and search large data sets from a generic "metric space", i.e. whE4 object proximity is only defined by a distance function satisfyingth positivity, symmetry, and triangle inequality postulates. We detail algorith[ for insertion
Results 1  10
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996,553