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The bi-criteria seeding algorithms for two variants of k-means problem

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  • Min Li

    (Shandong Normal University)

Abstract

The k-means problem is very classic and important in computer science and machine learning, so there are many variants presented depending on different backgrounds, such as the k-means problem with penalties, the spherical k-means clustering, and so on. Since the k-means problem is NP-hard, the research of its approximation algorithm is very hot. In this paper, we apply a bi-criteria seeding algorithm to both k-means problem with penalties and spherical k-means problem, and improve (upon) the performance guarantees given by the k-means++ algorithm for these two problems.

Suggested Citation

  • Min Li, 0. "The bi-criteria seeding algorithms for two variants of k-means problem," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-12.
    • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-020-00537-9
    DOI: 10.1007/s10878-020-00537-9
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    References listed on IDEAS

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    1. Min Li & Dachuan Xu & Jun Yue & Dongmei Zhang & Peng Zhang, 2020. "The seeding algorithm for k-means problem with penalties," Journal of Combinatorial Optimization, Springer, vol. 39(1), pages 15-32, January.
    2. Min Li & Dachuan Xu & Jun Yue & Dongmei Zhang, 2020. "The Parallel Seeding Algorithm for k-Means Problem with Penalties," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 37(04), pages 1-18, August.
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    Cited by:

    1. Peihuang Huang & Pei Yao & Zhendong Hao & Huihong Peng & Longkun Guo, 2021. "Improved Constrained k -Means Algorithm for Clustering with Domain Knowledge," Mathematics, MDPI, vol. 9(19), pages 1-14, September.

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