Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online …

In the real world, most of the applications are inherently dynamic in nature ie their underlying
data distribution changes with time. As a result, the concept drifts occur very frequently in the …

With unlimited growth of real-world data size and increasing requirement of real-time
processing, immediate processing of big stream data has become an urgent problem. In …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Stream data are sequence of data examples that continuously arrive at time-varying and
possibly unbound streams. These data streams are potentially huge in size and thus it is …

In the case of real-world data streams, the underlying data distribution will not be static; it is
subject to variation over time, which is known as the primary reason for concept drift …

This paper presents recurring concept drifts (RCD), a framework that offers an alternative
approach to handle data streams that suffer from recurring concept drifts (on-line learning). It …

Recent years have witnessed a large body of research work on mining concept drifting data
streams, where a primary assumption is that the up-to-date data chunk and the yet-to-come …

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 …

For most real‐world data streams, the concept about which data is obtained may shift from
time to time, a phenomenon known as concept drift. For most real‐world applications such …