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K-medians clustering

Known as: K-median problem, K-medians 
In statistics and data mining, k-medians clustering is a cluster analysis algorithm. It is a variation of k-means clustering where instead of… 
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Related topics

Related topics

11 relations
Cluster analysisData miningEnvironment for DeveLoping KDD-Applications Supported by Index-StructuresEuclidean distance

Broader (1)

Operations research

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Brain storm optimization algorithms with k-medians clustering algorithms
Brain storm optimization (BSO) algorithm is a novel swarm intelligence algorithm inspired by human beings' brainstorming process… 
Highly Cited
2010
Highly Cited
2010
Minimizing the Diameter of a Network Using Shortcut Edges
We study the problem of minimizing the diameter of a graph by adding k shortcut edges, for speeding up communication in an… 
2009
2009
Differentially Private Approximation Algorithms
Consider the following problem: given a metric space, some of whose points are “clients,” select a set of at most k facility… 
2008
2008
Adding Capacity Points to a Wireless Mesh Network Using Local Search
Wireless mesh network deployments are popular as a cost-effective means to provide broadband connectivity to large user… 
Highly Cited
2008
Highly Cited
2008
Simpler Analyses of Local Search Algorithms for Facility Location
We study local search algorithms for metric instances of facility location problems: the uncapacitated facility location problem… 
Highly Cited
2005
Highly Cited
2005
Approximating k-median with non-uniform capacities
In this paper we give a constant factor approximation algorithm for the capacitated k-median problem. Our algorithm produces a… 
Highly Cited
2005
Highly Cited
2005
The reverse greedy algorithm for the metric k-median problem
The Reverse Greedy algorithm (RGREEDY) for the k-median problem works as follows. It starts by placing facilities on all nodes… 
Highly Cited
2004
Highly Cited
2004
A k-Median Algorithm with Running Time Independent of Data Size
AbstractWe give a sampling-based algorithm for the k-Median problem, with running time O(k $$(\frac{{k^2 }}{ \in } \log k)^2… 
2001
2001
Generalized medians
2000
2000
Comparison of Four Initialization Techniques for the K -Medians Clustering Algorithm
Clustering in Metric Spaces can be conveniently performed by the so called k-medians method. It consists of a variant of the… 
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