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An LP-based k-means algorithm for balancing weighted point sets

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  • Borgwardt, S.
  • Brieden, A.
  • Gritzmann, P.

Abstract

The classical k-means algorithm for partitioning n points in Rd into k clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been observed in many scientific and business applications.

Suggested Citation

  • Borgwardt, S. & Brieden, A. & Gritzmann, P., 2017. "An LP-based k-means algorithm for balancing weighted point sets," European Journal of Operational Research, Elsevier, vol. 263(2), pages 349-355.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:2:p:349-355
    DOI: 10.1016/j.ejor.2017.04.054
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    References listed on IDEAS

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    1. Shmuel Onn & Leonard J. Schulman, 2001. "The Vector Partition Problem for Convex Objective Functions," Mathematics of Operations Research, INFORMS, vol. 26(3), pages 583-590, August.
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    Cited by:

    1. Steffen Borgwardt & Rafael M. Frongillo, 2019. "Power Diagram Detection with Applications to Information Elicitation," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 184-196, April.
    2. Xiangyan Kong & Zhen Zhang & Qilong Feng, 2023. "On parameterized approximation algorithms for balanced clustering," Journal of Combinatorial Optimization, Springer, vol. 45(1), pages 1-14, January.
    3. Robertson, Blair & Price, Chris, 2024. "One point per cluster spatially balanced sampling," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).

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