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 ...

In the realm of data science, clustering algorithms play a pivotal role in uncovering hidden patterns, segmenting data, and gaining insights into complex datasets. These algorithms are instrumental in ...

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 ...

Extremely fast evaluation of accuracy (extrinsic quality) measures for the [overlapping/fuzzy] clusterings (collections of groups of items): family of [mean] F1 measures (including Average F1-Score) ...

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in ...