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Gaussian Collaborative Fuzzy C-Means Clustering

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Abstract

For most FCM-based fuzzy clustering algorithms, several problems, such as noise, non-spherical clusters, and size-imbalanced clusters, are difficult to solve. Different fuzzy clustering algorithms are developed to deal with these problems from different perspectives. However, no comprehensive viewpoint to generalize these problems has been put forward. In this paper, we reveal the inherent deficiency of FCM and propose a new fuzzy clustering method called Gaussian Collaborative Fuzzy C-means (GCFCM) to solve these problems. In GCFCM, Gaussian mixture model (GMM) and collaborative technology are adopted to enhance the ability of recognizing the intrinsic structure of clusters. Experimental results confirm that GCFCM is effective in dealing with noise, non-spherical clusters, size-imbalanced clusters, and those also show excellent performance in dealing with real-world data sets.

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Authors and Affiliations

  1. Department of Automation, Xiamen University, Xiamen, China

    Yunlong Gao, Zhihao Wang & Huidui Li

  2. College of Information Engineer, Jimei University, Xiamen, China

    Jinyan Pan

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  1. Yunlong Gao
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  2. Zhihao Wang
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  3. Huidui Li
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  4. Jinyan Pan
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Correspondence to Jinyan Pan.

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Gao, Y., Wang, Z., Li, H. et al. Gaussian Collaborative Fuzzy C-Means Clustering. Int. J. Fuzzy Syst. 23, 2218–2234 (2021). https://doi.org/10.1007/s40815-021-01090-1

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