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An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm

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Abstract

Fuzzy c-means clustering algorithm (FCM), as the most widely used clustering algorithm, works by iteratively updating the membership degree and the cluster centers to improve the effectiveness of clustering. The performance of FCM algorithm is chiefly evaluated by intra-cluster compactness and inter-cluster separation. However, it has some defects such as high dependency on the initial cluster centers, sensitivity to the noise samples and outliers, difficulties in obtaining the optimization of hyperparameters, a fairly poor performance on datasets with the nonuniform distribution. The main purpose of this paper is to tackle these issues. The novelty of this paper is three-fold: 1) a new FCM clustering algorithm (i.e., CWAFCM) has been proposed, which has a good capability of performing the clustering on datasets with nonuniform distribution and reducing the high dependency on the initial cluster centers; 2) considering the merits of AFCM-SP in removing noise samples and CWAFCM in performing clustering on datasets with nonuniform distribution, a combination of the objective functions of these two clustering algorithms is developed to construct the hybrid AFCM algorithm; and 3) during the parameter setting by means of the PSO algorithm with time-varying acceleration coefficients (PSO-TVAC), a new index, namely adaptive clustering validity index (ACVI), is presented to describe the intra-cluster compactness and the inter-cluster separation in a proper manner. Experiments on six data sets in UCI and one artificial data set have been carried out with a comparison of five well-known FCM algorithms. Experimental results have demonstrated that the proposed hybrid AFCM with adaptive weights can more effectively enhance the performance of FCM to increase the clustering effectiveness than the contrastive algorithms. The ranks for seven algorithms on seven datasets in terms of CVIXB, accuracy, and normalized mutual information(NMI), further verifying the superiority of the new algorithm. Therefore it can be concluded that the proposed hybrid AFCM algorithm performs well in reducing the reliance on the initial cluster centers and the sensitivity to the noise and outliers.

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Acknowledgements

The authors thank the editor and anonymous reviewers for their detailed and constructive comments that help us to increase the quality of this work. This work was supported in part by the National Natural Science Foundation of China under Grant 61873169, in part by the Natural Science Foundation of Shanghai under Grant 18ZR1427100 and in part by the National Natural Science Foundation of China under Grants 62073223.

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Authors and Affiliations

  1. College Of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jianhua Hu, Huilin Yin & Guoliang Wei

  2. Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yan Song

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  1. Jianhua Hu
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  2. Huilin Yin
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Correspondence to Jianhua Hu.

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Hu, J., Yin, H., Wei, G. et al. An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm. Appl Intell 52, 9521–9536 (2022). https://doi.org/10.1007/s10489-021-02801-9

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