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A Novel Neighborhood Granular Meanshift Clustering Algorithm

Author

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  • Qiangqiang Chen

    (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China
    These authors contributed equally to this work.)

  • Linjie He

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China
    These authors contributed equally to this work.)

  • Yanan Diao

    (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China)

  • Kunbin Zhang

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China)

  • Guoru Zhao

    (CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Research Center for Neural Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
    Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Yumin Chen

    (College of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China)

Abstract

The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering algorithms are used to group samples into a number of classes or clusters based on the distances of the given sample features. Therefore, how to define the distance between samples is important for the clustering algorithm. Traditional clustering algorithms are generally based on the Mahalanobis distance and Minkowski distance, which have difficulty dealing with set-based data and uncertain nonlinear data. To solve this problem, we propose the granular vectors relative distance and granular vectors absolute distance based on the neighborhood granule operation. Further, the neighborhood granular meanshift clustering algorithm is also proposed. Finally, the effectiveness of neighborhood granular meanshift clustering is proved from two aspects of internal metrics (Accuracy and Fowlkes–Mallows Index) and external metric (Silhouette Coeffificient) on multiple datasets from UC Irvine Machine Learning Repository (UCI). We find that the granular meanshift clustering algorithm has a better clustering effect than the traditional clustering algorithms, such as Kmeans, Gaussian Mixture and so on.

Suggested Citation

  • Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:207-:d:1021189
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    References listed on IDEAS

    as
    1. Vassilis G. Kaburlasos & Chris Lytridis & Eleni Vrochidou & Christos Bazinas & George A. Papakostas & Anna Lekova & Omar Bouattane & Mohamed Youssfi & Takashi Hashimoto, 2021. "Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures," Mathematics, MDPI, vol. 9(22), pages 1-23, November.
    2. Wei Li & Xiaoyu Ma & Yumin Chen & Bin Dai & Runjing Chen & Chao Tang & Youmeng Luo & Kaiqiang Zhang, 2021. "Random Fuzzy Granular Decision Tree," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, June.
    3. Linjie He & Yumin Chen & Caiming Zhong & Keshou Wu, 2022. "Granular Elastic Network Regression with Stochastic Gradient Descent," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
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    Cited by:

    1. Jian Chen & Yongkun Shi & Jiaquan Sun & Jiangkuan Li & Jing Xu, 2023. "Base Station Planning Based on Region Division and Mean Shift Clustering," Mathematics, MDPI, vol. 11(8), pages 1-22, April.

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