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Joint learning of fuzzy embedded clustering and non-negative spectral clustering

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Abstract

Fuzzy k-means clustering is widely acknowledged for its remarkable performance in data clustering. However, its effectiveness must improve when dealing with high-dimensional data characterized by complex distributions, leading to subpar clustering results. To tackle this challenge, we introduce a novel clustering method named Joint Learning of Fuzzy Embedded Clustering and Non-negative Spectral Clustering (FECNSC). Initially, FECNSC utilizes rapid spectral embedding to reduce the dimensionality of the data. Subsequently, it incorporates fuzzy clustering and non-negative spectral clustering in a unified framework. The novel fuzzy clustering method enhances fuzzy membership by regularising of non-negative spectral clustering. Our experimental results demonstrate the overall superiority of FECNSC in terms of accuracy, normalized mutual information, and purity across various benchmark datasets, surpassing multiple advanced methods. Therefore, FECNSC is an efficient solution for managing data with complex distributions.

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Notes

  1. http://archive.ics.uci.edu/ml/index.php

  2. https://www.csie.ntu.edu.tw/cjlin/libsvmtools/datasets/multiclass.html

  3. http://www.cs.columbia.edu/CAVE/software/softlib/

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Funding

This work was supported in part by the Key Area Research and Development Program of Guangdong Province (Grant numbers [2018B030338001] and [2018B010115002]), in part by the Basic and Applied Basic Research Project of Guangzhou Basic Research Program (Grant numbers [202201010595]), in part by the Guangdong Education Department in the Guangdong University of Technology(Grant numbers [220413548]).

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Authors and Affiliations

  1. School of Integrated Circuits, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China

    Wujian Ye, Jiada Wang & Yijun Liu

  2. Big Data Institute, Shenzhen University, Shenzhen, Guangdong, 518060, China

    Yongda Cai

  3. Peng Cheng Laboratory, Shenzhen, Guangdong, 518000, China

    Huihui Zhou

  4. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, Taiwan, China

    Chin-chen Chang

Authors
  1. Wujian Ye
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  2. Jiada Wang
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  3. Yongda Cai
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  4. Yijun Liu
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  5. Huihui Zhou
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  6. Chin-chen Chang
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Contributions

Wujian Ye: Investigation, Software, Writing - original draft. Jiada Wang: Conceptualization, Methodology. Yongda Cai: Software, Writing - original draft. Yijun Liu: Investigation, Methodology, Writing - original draft. Chin-chen Chang: Supervision.

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Correspondence to Jiada Wang.

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Ye, W., Wang, J., Cai, Y. et al. Joint learning of fuzzy embedded clustering and non-negative spectral clustering. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17909-y

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