The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

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 …

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 …

Building a successful recommender system depends on understanding both the dimensions
of people's preferences as well as their dynamics. In certain domains, such as fashion …

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 …

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 …

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 …

With the wide spread of sensors and smart devices in recent years, the data generation
speed of the Internet of Things (IoT) systems has increased dramatically. In IoT systems …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …