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 …

The significant growth of interconnected Internet‐of‐Things (IoT) devices, the use of social
networks, along with the evolution of technology in different domains, lead to a rise in the …

In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …

Data stream mining is an active research area that has recently emerged to discover
knowledge from large amounts of continuously generated data. In this context, several data …

Abstract Massive Online Analysis (MOA) is a software environment for implementing
algorithms and running experiments for online learning from evolving data streams. MOA is …

Nowadays, with the advance of technology, many applications generate huge amounts of
data streams at very high speed. Examples include network traffic, web click streams, video …

Clustering data streams has drawn lots of attention in the last few years due to their ever-
growing presence. Data streams put additional challenges on clustering such as limited time …

As more and more applications produce streaming data, clustering data streams has
become an important technique for data and knowledge engineering. A typical approach is …

Clustering algorithms have become one of the most critical research areas in multiple
domains, especially data mining. However, with the massive growth of big data applications …

A data stream is a continuously arriving sequence of data and clustering data streams
requires additional considerations to traditional clustering. A stream is potentially …