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 …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …

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 …

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 …

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 …

Analyzing data streams has received considerable attention over the past decades due to
the widespread usage of sensors, social media and other streaming data sources. A core …

Industrial Applications of Machine Learning shows how machine learning can be applied to
address real-world problems in the fourth industrial revolution, and provides the required …

In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Data stream clustering is a hot research area due to the abundance of data streams
collected nowadays and the need for understanding and acting upon such sort of data …