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Improved fuzzy C-means algorithm based on density peak

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Abstract

Fuzzy C-means (FCM) algorithm is a fuzzy clustering algorithm based on objective function compared with typical “hard clustering” such as k-means algorithm. FCM algorithm calculates the membership degree of each sample to all classes and obtain more reliable and accurate classification results. However, in the process of clustering, FCM algorithm needs to determine the number of clusters manually, and is sensitive to the initial clustering center. It is easy to generate problems such as multiple clustering iterations, slow convergence speed and local optimal solution. To address those problems, we propose to combine the FCM algorithm and DPC (Clustering by fast search and find of density peaks) algorithm. First, DPC algorithm is used to automatically select the center and number of clusters, and then FCM algorithm is used to realize clustering. The comparison experiments show that the improved FCM algorithm has a faster convergence speed and higher accuracy.

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by Shandong Provincial Natural Science Foundation of China under Grant ZR2018MF009, The State Key Research Development Program of China under Grant 2017YFC0804406, National Natural Science Foundation of China under Grant 91746104, the Special Funds of Taishan Scholars Construction Project, and Leading Talent Project of Shandong University of Science and Technology.

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Liu, Xy., Fan, Jc. & Chen, Zw. Improved fuzzy C-means algorithm based on density peak. Int. J. Mach. Learn. & Cyber. 11, 545–552 (2020). https://doi.org/10.1007/s13042-019-00993-8

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