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Improved Constrained k -Means Algorithm for Clustering with Domain Knowledge

Author

Listed:
  • Peihuang Huang

    (College of Mathematics and Data Science, Minjiang University, Fuzhou 350116, China
    These authors contributed equally to this work.)

  • Pei Yao

    (College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
    These authors contributed equally to this work.)

  • Zhendong Hao

    (College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
    These authors contributed equally to this work.)

  • Huihong Peng

    (College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China
    These authors contributed equally to this work.)

  • Longkun Guo

    (School of Computer Science, Qilu University of Technology, Jinan 250353, China
    These authors contributed equally to this work.)

Abstract

Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy rate despite its advantage of fast procession. In this paper, we model domain knowledge (i.e., background knowledge or side information), respecting some applications as must-link and cannot-link sets, for the sake of collaborating with k -means for better accuracy. We first propose an algorithm for constrained k -means, considering only must-links. The key idea is to consider a set of data points constrained by the must-links as a single data point with a weight equal to the weight sum of the constrained points. Then, for clustering the data points set with cannot-link, we employ minimum-weight matching to assign the data points to the existing clusters. At last, we carried out a numerical simulation to evaluate the proposed algorithms against the UCI datasets, demonstrating that our method outperforms the previous algorithms for constrained k -means as well as the traditional k -means regarding the clustering accuracy rate although with a slightly compromised practical runtime.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2390-:d:643269
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    References listed on IDEAS

    as
    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, 0. "The bi-criteria seeding algorithms for two variants of k-means problem," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-12.
    3. Volodymyr Melnykov & Xuwen Zhu, 2019. "An extension of the K-means algorithm to clustering skewed data," Computational Statistics, Springer, vol. 34(1), pages 373-394, March.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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