Gaussian kernel fuzzy c-means with width parameter computation and regularization
EC Simões, FAT de Carvalho - Pattern Recognition, 2023 - Elsevier
The conventional Gaussian kernel fuzzy c-means clustering algorithms require selecting the
width hyper-parameter, which is data-dependent and fixed for the entire execution. Not only …
width hyper-parameter, which is data-dependent and fixed for the entire execution. Not only …
A reliable region information driven kriging-assisted multiobjective rough fuzzy clustering algorithm for color image segmentation
F Zhao, Z Tang, H Liu, Z Xiao, J Fan - Expert Systems with Applications, 2023 - Elsevier
Multiobjective clustering algorithms (MOCAs) are becoming increasingly popular with the
merit of segmenting images from multiple perspectives. The performances of MOCAs highly …
merit of segmenting images from multiple perspectives. The performances of MOCAs highly …
[PDF][PDF] Segmentation of brain tissue using improved kernelized rough-fuzzy c-means technique
Brain magnetic resonance imaging (MRI) data is a hot topic in the domains of biomedical
engineering and machine learning. Without locating anomalies, such as tumors and edema …
engineering and machine learning. Without locating anomalies, such as tumors and edema …
Research on fuzzy clustering based on improved sparrow algorithm
S Qiu, R Li - International Conference on Algorithms …, 2022 - spiedigitallibrary.org
Focusing the problem that the traditional fuzzy c-means clustering (FCM), which is a local
search algorithm, it uses iterative hill climbing technology, which is sensitive to initial values …
search algorithm, it uses iterative hill climbing technology, which is sensitive to initial values …