Since its inception in 1965, the theory of fuzzy sets has advanced in a variety of ways and in
many disciplines. Applications of this theory can be found, for example, in artificial …

Computational Intelligence comprises concepts, paradigms, algorithms, and
implementations of systems that are supposed to exhibit intelligent behavior in complex …

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 …

Numerical possibility distributions can encode special convex families of probability
measures. The connection between possibility theory and probability theory is potentially …

Clustering is one of the most used tools in data analysis. In the last decades, due to the
increasing complexity of data, soft clustering has received a great deal of attention. There …

A modified possibilistic fuzzy c-means clustering algorithm is presented for fuzzy
segmentation of magnetic resonance (MR) images that have been corrupted by intensity …

The popularity and applicability of mobile crowdsensing applications are continuously
increasing due to the widespread of mobile devices and their sensing and processing …

Pattern recognition is a collection of computer techniques to classify various observations
into different clusters of similar attributes in either supervised or unsupervised manner …

An encompassing, self-contained introduction to the foundations of the broad field of fuzzy
clustering is presented. The fuzzy cluster partitions are introduced with special emphasis on …

Possibilistic fuzzy c-means clustering (PFCM) is an unsupervised hybrid clustering
algorithm, which can partly inherit the stability of fuzzy c-means clustering (FCM) algorithm …