Mathematical Programming

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

Probably certifiably correct k-means clustering

  • Takayuki Iguchi
  • Dustin G. MixonEmail author
  • 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 



The authors thank the anonymous referees, whose suggestions significantly improved this paper’s presentation and literature review. The authors also thank Afonso S. Bandeira and Nicolas Boumal for interesting discussions and valuable comments on an earlier version of this manuscript, and Xiaodong Li and Yang Li for interesting comments on our dual certificate. DGM was supported by an AFOSR Young Investigator Research Program award, NSF Grant No. DMS-1321779, and AFOSR Grant No. F4FGA05076J002. SV was supported by Rachel Ward’s NSF CAREER award and AFOSR Young Investigator Research Program award. The views expressed in this article are those of the authors and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the U.S. Government.


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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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