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 …

Large volumes of spatio-temporal data are increasingly collected and studied in diverse
domains, including climate science, social sciences, neuroscience, epidemiology …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Time series anomaly detection remains a perennially important research topic. If anything, it
is a task that has become increasingly important in the burgeoning age of IoT. While there …

Explosive growth in geospatial and temporal data as well as the emergence of new
technologies emphasize the need for automated discovery of spatiotemporal knowledge …

Metro systems have become one of the most important public transit services in cities. It is
important to understand individual metro passengers' spatio-temporal travel patterns. More …

The detection of outliers in spatio-temporal traffic data is an important research problem in
the data mining and knowledge discovery community. However to the best of our …

Time series anomaly detection remains one of the most active areas of research in data
mining. In spite of the dozens of creative solutions proposed for this problem, recent …