Data stream research has grown rapidly over the last decade. Two major features
distinguish data stream from batch learning: stream data are generated on the fly, possibly in …

Usually concept drift occurs in many applications of machine learning. Detecting a concept
drift is the main challenge in a data stream because of the high speed and their large size …

Nowadays with a growing number of online controlling systems in the organization and also
a high demand of monitoring and stats facilities that uses data streams to log and control …

Mining data streams for predictive analysis is one of the most interesting topics in machine
learning. With the drifting data distributions, it becomes important to build adaptive systems …

Increasing access to incredibly large, nonstationary datasets and corresponding demands to
analyse these data has led to the development of new online algorithms for performing …

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 As data evolve over time, static models become outdated.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 …

Recently advancement in hardware and software has enabled processing of large amount
of data efficiently. Many applications generate big data rapidly in high fluctuating rates. The …

Data stream is a typical big data. Data stream can be founded in many real-life applications,
such as wireless sensor networks, power consumption, information security and financial …

Concept drift is a major problem in online learning due to its impact on the predictive
performance of data stream mining systems. Recent studies have started exploring data …

Data streams are viewed as a sequence of relational tuples (eg, sensor readings, call
records, web page visits) that continuously arrive at time-varying and possibly unbound …