A possibilistic fuzzy Gath-Geva clustering algorithm using the exponential distance

作者：

Highlights：

• Propose PFGG clustering algorithm.

• PFGG uses the exponential distance in contrast to PFCM.

• PFGG clusters the dataset containing noisy data accurately.

• The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM.

摘要

•Propose PFGG clustering algorithm.•PFGG uses the exponential distance in contrast to PFCM.•PFGG clusters the dataset containing noisy data accurately.•The clustering accuracies, cluster centers and convergence results of PFGG are significantly better than FCM, GG, PFCM.

论文关键词：Noise data,Hyperellipsoidal data,GG clustering,PFCM clustering,PFGG clustering

论文评审过程：Received 13 July 2020, Revised 19 May 2021, Accepted 1 July 2021, Available online 7 July 2021, Version of Record 14 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115550