In evolving data stream, when its concept undergoes a change it is known as concept drift.
Detecting concept drift and handling it is a challenging task in data stream mining. If an …

Detecting the changes and reacting on them is an interesting research topic in current era.
Concept drift detection is comes under data stream mining. Process which takeout …

Data stream mining is an important research topic that has received increasing attention due
to its use in a wide range of applications, such as sensor networks, banking, and …

Abstract Concept drift in data stream poses many challenges and difficulties in mining this
tradition-distinct database. In this paper, we focus on detecting concept drift in evolving data …

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 …

Abstract Concept drift, change in the underlying distribution that data points come from, is an
inevitable phenomenon in data streams. Due to increase in the number of data streams' …

The interest in data stream mining is continuously growing due to the increasing volume of
data arriving at high speed produced by various systems. Processing and classification to …

Drift detection methods identify changes in data streams. Such changes are called concept
drifts. Existing drift detection methods often assume that the input is a noise-free data stream …

In data stream mining, one of the biggest challenges is to develop algorithms that deal with
the changing data. As data evolve over time, static models become outdated. This …

With the advancement of information society, a large amount of data, which is in the form of
stream, has been produced in many fields. As a result of its extensive application in the …