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An equidistance index intuitionistic fuzzy c-means clustering algorithm based on local density and membership degree boundary

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Abstract

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm that effectively expresses complex real world information by integrating fuzzy parameters. Due to its simplicity and operability, it is widely used in multiple fields such as image segmentation, text categorization, pattern recognition and others. The intuitionistic fuzzy c-means (IFCM) clustering has been proven to exhibit better performance than FCM due to further capturing uncertain information in the dataset. However, the IFCM algorithm has limitations such as the random initialization of cluster centers and the unrestricted influence of all samples on all cluster centers. Therefore, a novel algorithm named equidistance index IFCM (EI-IFCM) is proposed for improving shortcomings of the IFCM. Firstly, the EI-IFCM can commence its learning process from more superior initial clustering centers. The EI-IFCM algorithm organizes the initial cluster centers based on the contribution of local density information from the data samples. Secondly, the membership degree boundary is assigned for the data samples satisfying the equidistance index to avoid the unrestricted influence of all samples on all cluster centers in the clustering process. Finally, the performance of the proposed EI-IFCM is numerically validated using UCI datasets which contain data from healthcare, plant, animal, and geography. The experimental results indicate that the proposed algorithm is competitive and suitable for fields such as plant clustering, medical classification, image differentiation and others. The experimental results also indicate that the proposed algorithm is surpassing in terms of iteration and precision in the mentioned fields by comparison with other efficient clustering algorithms.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China with grant number of 62173025 and Key Research and Development Project of Guangdong Province with grant number of 2021B0101420003.

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Qianxia Ma, Xiaomin Zhu & Xiangkun Zhao

  2. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Butian Zhao & Runtong Zhang

  3. Rail Transit Department, Tianjin Jinhang Computing Technology Research Institute, Tianjin, 300308, China

    Guanhua Fu

Authors
  1. Qianxia Ma
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  2. Xiaomin Zhu
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  3. Xiangkun Zhao
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  4. Butian Zhao
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Contributions

Qianxia Ma: Methodology, Writing – original draft, Software, Validation, Formal analysis, Investigation, Visualization, Supervision. Xiaomin Zhu: Resources, Supervision, Project administration. Xiangkun Zhao: Software, Investigation, Writing – review & editing. Butian Zhao: Software, Investigation, Writing – review & editing. Guanhua Fu: Resources, Supervision, Project administration. Runtong Zhang: Resources, Supervision, Project administration.

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Correspondence to Xiaomin Zhu.

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Ma, Q., Zhu, X., Zhao, X. et al. An equidistance index intuitionistic fuzzy c-means clustering algorithm based on local density and membership degree boundary. Appl Intell 54, 3205–3221 (2024). https://doi.org/10.1007/s10489-024-05297-1

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