A local search approximation algorithm for k-means clustering

In k-means clustering we are given a set of n data points in d-dimensional space R d and an integer k, and the problem is to determine a set of k points in R d , called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms a...

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Vydáno v:	Computational geometry : theory and applications Ročník 28; číslo 2; s. 89 - 112
Hlavní autoři:	Kanungo, Tapas, Mount, David M., Netanyahu, Nathan S., Piatko, Christine D., Silverman, Ruth, Wu, Angela Y.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.06.2004
Témata:	Approximation algorithms Clustering Computational geometry k-means Local search k-means Local search Computational geometry Approximation algorithms Clustering
ISSN:	0925-7721
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Abstract	In k-means clustering we are given a set of n data points in d-dimensional space R d and an integer k, and the problem is to determine a set of k points in R d , called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the very high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance. We consider the question of whether there exists a simple and practical approximation algorithm for k-means clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9+ ε)-approximation algorithm. We present an example showing that any approach based on performing a fixed number of swaps achieves an approximation factor of at least (9− ε) in all sufficiently high dimensions. Thus, our approximation factor is almost tight for algorithms based on performing a fixed number of swaps. To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with Lloyd's algorithm, this heuristic performs quite well in practice.
AbstractList	In k-means clustering we are given a set of n data points in d-dimensional space R d and an integer k, and the problem is to determine a set of k points in R d , called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the very high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance. We consider the question of whether there exists a simple and practical approximation algorithm for k-means clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9+ ε)-approximation algorithm. We present an example showing that any approach based on performing a fixed number of swaps achieves an approximation factor of at least (9− ε) in all sufficiently high dimensions. Thus, our approximation factor is almost tight for algorithms based on performing a fixed number of swaps. To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with Lloyd's algorithm, this heuristic performs quite well in practice.
Author	Silverman, Ruth Wu, Angela Y. Piatko, Christine D. Kanungo, Tapas Mount, David M. Netanyahu, Nathan S.
Author_xml	– sequence: 1 givenname: Tapas surname: Kanungo fullname: Kanungo, Tapas email: kanungo@almaden.ibm.com organization: IBM Almaden Research Center, San Jose, CA 95120, USA – sequence: 2 givenname: David M. surname: Mount fullname: Mount, David M. email: mount@cs.umd.edu organization: Department of Computer Science, University of Maryland, College Park, MD, USA – sequence: 3 givenname: Nathan S. surname: Netanyahu fullname: Netanyahu, Nathan S. email: nathan@macs.biu.ac.il organization: Department of Mathematics and Computer Science, Bar-Ilan University, Ramat-Gan 52900, Israel – sequence: 4 givenname: Christine D. surname: Piatko fullname: Piatko, Christine D. email: christine.piatko@jhuapl.edu organization: The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA – sequence: 5 givenname: Ruth surname: Silverman fullname: Silverman, Ruth email: ruth@cfar.umd.edu organization: Center for Automation Research, University of Maryland, College Park, MD, USA – sequence: 6 givenname: Angela Y. surname: Wu fullname: Wu, Angela Y. email: awu@american.edu organization: Department of Computer Science, American University, Washington, DC, USA
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Snippet	In k-means clustering we are given a set of n data points in d-dimensional space R d and an integer k, and the problem is to determine a set of k points in ...
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SubjectTerms	Approximation algorithms Clustering Computational geometry k-means Local search
Title	A local search approximation algorithm for k-means clustering
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