A possibilistic fuzzy c-means clustering algorithm

By Nikhil Ranjan Pal, K. Pal, James M. Keller, James C. Bezdek. IEEE transactions on fuzzy systems, 2005

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Oil-paper aging evaluation by fuzzy clustering and factor analysis to statistical parameters of partial discharges (2010, methodology): “To solve the problem, the use of kernel fuzzy c-means (KFCM) and possibilistic fuzzy c-means (PFCM) is planned [19, 21, 22].”
Prototype-based Neural Network Layers: Incorporating Vector Quantization (2018, methodology): “ovements of VQ methods were invented. Forexamplemethodswithimprovedconvergencebehavior[21,22],includingneuralgas[23],fuzzy variants where the crisp WTA-rule is replaced by a smoothed decision function[24, 25, 26, 27] and variants that use a different metric than the Euclidean distance [28, 29]. A sy”
Dual Fuzzy-Possibilistic Co-clustering for Document Categorization (2007, background): “This absoluteness is the unique characteristic of a typicality measure [14], which is shared by the possibilistic memberships of all existing possibilistic clustering algorithms.”