IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p207-d1021189.html
   My bibliography  Save this article

A Novel Neighborhood Granular Meanshift Clustering Algorithm

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/207/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/1/207/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eleni Vrochidou & Viktoria Nikoleta Tsakalidou & Ioannis Kalathas & Theodoros Gkrimpizis & Theodore Pachidis & Vassilis G. Kaburlasos, 2022. "An Overview of End Effectors in Agricultural Robotic Harvesting Systems," Agriculture, MDPI, vol. 12(8), pages 1-35, August.
    2. Xiyang Yang & Shiqing Zhang & Xinjun Zhang & Fusheng Yu, 2022. "Polynomial Fuzzy Information Granule-Based Time Series Prediction," Mathematics, MDPI, vol. 10(23), pages 1-21, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:207-:d:1021189. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.