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

In this paper, we introduce a new machine-learning-based data classification algorithm that
is applied to network intrusion detection. The basic task is to classify network activities (in the …

This paper presents a self-adaptive autonomous online learning through a general type-2
fuzzy system (GT2 FS) for the motor imagery (MI) decoding of a brain-machine interface …

Synthetic aperture radar (SAR) images are a valuable source of information for the detection
of marine oil spills. For their effective analysis, it is important to have segmentation …

Fuzzy clustering is used extensively in several domains of research. In the literature, starting
from the well-known fuzzy k-means (fk m) clustering algorithm, an increasing number of …

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 …

Basic ideas and formal concepts from fuzzy sets and fuzzy logic have been used
successfully in various branches of science and engineering. This paper elaborates on the …

Application of clustering algorithms to extract or summarize data from large data sets is a
straightforward and effective approach. Sometimes data sets are not only large in size but …

Clustering is an unsupervised learning task that aims at decomposing a given set of objects
into subgroups or clusters based on similarity. The goal is to divide the data-set in such a …

Type-2 fuzzy sets have drawn increasing research attentions in the pattern recognition
community, since it is capable of modeling various uncertainties that cannot be appropriately …