In a dynamic stream there is an assumption that the underlying process generating the
stream is non-stationary and that concepts within the stream will drift and change as the …

Many practical applications, such as social media and monitoring system, will constantly
generate streaming data, which has problems of instability, lack of labels and multiclass …

Cluster in graphs is densely connected group of vertices sparsely connected to other
groups. Hence, for prediction of a future link between a pair of vertices, these vertices …

Abstract In cooperative Peer-to-Peer (P2P) networks, a number of users, called Free-riders,
try to receive service from others without cooperating with them. Some others, called Sybil …

The dynamic nature and time-varying behavior of actual environments provide serious
challenges for learning models. Thus, changes may deteriorate the constructed control …

Learning in data streams has practical significance in today's knowledge intensive era.
Unlike static data mining, data stream mining requires handling with the critical issues …

Non-stationary classification problems concern the changes on data distribution over a
classifier lifetime. To face this problem, learning algorithms must conciliate essential, but …

Conventional network traffic detection methods based on data mining could not efficiently
handle high throughput traffic with concept drift. Data stream mining techniques are able to …

Background Traditional data classification techniques usually divide the data space into sub-
spaces, each representing a class. Such a division is carried out considering only physical …

Non-stationary data can be characterized as data having a distribution that changes over
time. It is well-known that most successful machine learning algorithms are based on …