Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

While machine learning and artificial intelligence have long been applied in networking
research, the bulk of such works has focused on supervised learning. Recently, there has …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Density based clustering algorithms (DBCLAs) rely on the notion of density to identify
clusters of arbitrary shapes, sizes with varying densities. Existing surveys on DBCLAs cover …

This article describes the implementation and use of the R package dbscan, which provides
complete and fast implementations of the popular density-based clustering algorithm …