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 …

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 …

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 …

Learning non-stationary data streams is challenging due to the unique characteristics of
infinite length and evolving property. Current existing works often concentrate on the …

Many real world problems involve the challenging context of data streams, where classifiers
must be incremental: able to learn from a theoretically-infinite stream of examples using …

Data streams, which can be considered as one of the primary sources of what is called big
data, arrive continuously with high speed. The biggest challenge in data streams mining is to …

Learning from data streams requires efficient algorithms capable of deriving a model
accordingly to the arrival of new instances. Data streams are by definition unbounded …

Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

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 …

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 …