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 …

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 …

Abstract The concept of Data Stream has emerged as a result of the evolution of
technologies in different domains such as banking, e-commerce, social media, and many …

Ensemble Methods grows along with Machine Learning and Computational Intelligence
domain proves to be effective and versatile. It helps in reducing variance and improves …

Mining in data stream plays a vital role in Big Data analytics. Traffic management, sensor
networks and monitoring, weblogs analysis are the application of dynamic environments …

Data Streams are unbounded, sequential data instances that are generated very rapidly.
The storage, querying and mining of such rapid flows of data is computationally very …

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 …

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 …

It is challenging to use traditional data mining techniques to deal with real-time data stream
classifications. Existing mining classifiers need to be updated frequently to adapt to the …

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 …