Mathematical Programming

, Volume 165, Issue 2, pp 605–642 | Cite as

Probably certifiably correct k-means clustering

  • Takayuki Iguchi
  • Dustin G. Mixon
  • Jesse Peterson
  • Soledad Villar
Full Length Paper Series A


Recently, Bandeira (C R Math, 2015) introduced a new type of algorithm (the so-called probably certifiably correct algorithm) that combines fast solvers with the optimality certificates provided by convex relaxations. In this paper, we devise such an algorithm for the problem of k-means clustering. First, we prove that Peng and Wei’s semidefinite relaxation of k-means Peng and Wei (SIAM J Optim 18(1):186–205, 2007) is tight with high probability under a distribution of planted clusters called the stochastic ball model. Our proof follows from a new dual certificate for integral solutions of this semidefinite program. Next, we show how to test the optimality of a proposed k-means solution using this dual certificate in quasilinear time. Finally, we analyze a version of spectral clustering from Peng and Wei (SIAM J Optim 18(1):186–205, 2007) that is designed to solve k-means in the case of two clusters. In particular, we show that this quasilinear-time method typically recovers planted clusters under the stochastic ball model.

Mathematics Subject Classification

65-XX 90-XX 46N10 68Q87 


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Copyright information

© Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society (outside the USA) 2016

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsAir Force Institute of TechnologyWright-Patterson AFBUSA
  2. 2.Department of MathematicsUniversity of Texas at AustinAustinUSA

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