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Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W function

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Abstract

Interval type-2 fuzzy sets not only have stronger ability to deal with uncertainty, but also have low computational complexity than general type-2 fuzzy set, so they are widely used in fuzzy clustering methods. However, most existing interval type-2 fuzzy clustering methods are still sensitive to noise and lack a certain degree of robustness in segmenting images with noise. Therefore, this paper proposes a novel interval type-2 enhanced kernel possibilistic fuzzy local and non-local information c-means clustering method for segmenting images with high noise. Interval type-2 possibilistic fuzzy clustering with Lambert-W function is first extended to obtain a novel interval type-2 enhanced possibilistic fuzzy clustering with product partition. Then deep local neighborhood information including local and non-local information is used to constrain interval type-2 enhanced possibilistic fuzzy product partition c-means clustering, and a robust interval type-2 enhanced possibilistic fuzzy deep local information clustering with kernel metric is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms the latest fuzzy clustering-related algorithms in the presence of high noise.

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Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62071378), and the Shaanxi Natural Science Foundation of China (2022JM-370). The authors would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, the authors would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China for financially support.

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  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, People’s Republic of China

    Chengmao Wu, Siyun Peng & Xialu Zhang

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Contributions

CW: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Visualization. SP: Formal analysis, Investigation, Software, Writing—original draft, Writing—review & editing. XZ: Formal analysis, Investigation, Software, Writing-review.

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Correspondence to Siyun Peng.

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Wu, C., Peng, S. & Zhang, X. Robust interval type-2 kernel-based possibilistic fuzzy deep local information clustering driven by Lambert-W function. Vis Comput 40, 2161–2201 (2024). https://doi.org/10.1007/s00371-023-02910-1

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