A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

Abstract: In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the ...

if you use fuzzy-c-means package in your paper, please cite it in your publication. @software{dias2019fuzzy, author = {Madson Luiz Dantas Dias}, title = {fuzzy-c ...

ABSTRACT: Domaining is a crucial process in geostatistics, particularly when significant spatial variations are observed within a site, as these variations can significantly affect the outcomes of ...

Abstract: With the rapid and vigorous development of fuzzy clustering theory and methods, more fuzzy clustering algorithms have been proposed to establish the uncertainty description of the samples.

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle ...

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the ...