The processing of streaming data implies new requirements concerning restrictive
processing time, limited amount of memory and one scan of incoming instances. One of the …

Learning from data streams in the presence of concept drift is among the biggest challenges
of contemporary machine learning. Algorithms designed for such scenarios must take into …

The treatment of large data streams in the presence of concept drifts is one of the main
challenges in the field of data mining, particularly when the algorithms have to deal with …

The rapid growth of the information technology accelerates organizations to generate vast
volumes of high-velocity data streams. The concept drift is a crucial issue, and discovering …

With the wide application of machine learning algorithms to the real world, class imbalance
and concept drift have become crucial learning issues. Applications in various domains such …

Mining concept drifting data stream is a challenging area for data mining research. In real
world, data streams are not stable but change with time. Such changes termed as drifts in …

The digital universe is growing rapidly. The volume of data generated per annum is in the
order of zeta bytes due to the proliferation of the Internet. Many real-world applications …

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 …

Abstract Knowledge extraction from data streams has attracted attention in recent years due
to its wide range of applications, including sensor networks, web clickstreams, and user …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …