In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

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 …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Class evolution, the phenomenon of class emergence and disappearance, is an important
research topic for data stream mining. All previous studies implicitly regard class evolution …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

In recent years, a variety of research areas have contributed to a set of related problems with
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

Activity recognition aims to provide accurate and opportune information on people's
activities by leveraging sensory data available in today's sensory rich environments …

Outlier detection is of considerable interest in fields such as physical sciences, medical
diagnosis, surveillance detection, fraud detection and network anomaly detection. The data …