Kernel-based learning (KBL) methods have recently become prevalent in many engineering
applications, notably in signal processing and communications. The increased interest is …

Considering that weighted possibilistic fuzzy clustering does not obtain significant
performance compared with possibilistic fuzzy clustering, so this paper proposes an …

This study proposes an efficient anomalous behaviour detection framework using trajectory
analysis. Such framework includes the trajectory pattern learning module and the online …

The presence of noise or outliers in data sets may heavily affect the performance of
clustering algorithms and lead to unsatisfactory results. The majority of conventional …

Safety is a top priority for civil aviation. Data mining in digital Flight Data Recorder (FDR) or
Quick Access Recorder (QAR) data, commonly referred to as black box data on aircraft, has …

Subject to the inherent high uncertainty of wind, the prediction for its speed and direction
may be insufficiently accurate, the resulting decision actions of active yaw control (AYC) may …

This book examines signal processing techniques for cognitive radios. The book is divided
into three parts: Part I, is an introduction to cognitive radios and presents a history of the …

We present ClusterSVDD, a methodology that unifies support vector data descriptions
(SVDDs) and k-means clustering into a single formulation. This allows both methods to …

We consider the problem of clustering data sets in the presence of arbitrary outliers.
Traditional clustering algorithms such as k-means and spectral clustering are known to …

Over the last years, many variations of the quadratic k-means clustering procedure have
been proposed, all aiming to robustify the performance of the algorithm in the presence of …