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The effectiveness of lloyd-type methods for the k-means problem

Authors:

Rafail Ostrovsky,

Leonard J. Schulman,

Chaitanya SwamyAuthors Info & Claims

Journal of the ACM (JACM), Volume 59, Issue 6

Article No.: 28, Pages 1 - 22

https://doi.org/10.1145/2395116.2395117

Published: 09 January 2013 Publication History

Abstract

We investigate variants of Lloyd's heuristic for clustering high-dimensional data in an attempt to explain its popularity (a half century after its introduction) among practitioners, and in order to suggest improvements in its application. We propose and justify a clusterability criterion for data sets. We present variants of Lloyd's heuristic that quickly lead to provably near-optimal clustering solutions when applied to well-clusterable instances. This is the first performance guarantee for a variant of Lloyd's heuristic. The provision of a guarantee on output quality does not come at the expense of speed: some of our algorithms are candidates for being faster in practice than currently used variants of Lloyd's method. In addition, our other algorithms are faster on well-clusterable instances than recently proposed approximation algorithms, while maintaining similar guarantees on clustering quality. Our main algorithmic contribution is a novel probabilistic seeding process for the starting configuration of a Lloyd-type iteration.

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cover image Journal of the ACM

Journal of the ACM Volume 59, Issue 6

December 2012

213 pages

ISSN:0004-5411

EISSN:1557-735X

DOI:10.1145/2395116

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Copyright © 2013 ACM.

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Publication History

Published: 09 January 2013

Accepted: 01 September 2012

Revised: 01 September 2012

Received: 01 March 2010

Published in JACM Volume 59, Issue 6

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https://doi.org/10.1109/MSMC.2024.3443535
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