Predicting online on data streams, with data flowing continuously, quickly, and in large
quantities, is becoming increasingly more important in tackling real-world problems. In such …

This paper addresses the task of learning concept descriptions from streams of data. As new
data are obtained the concept description has to be updated regularly to include the new …

Abstract Knowledge extraction from data streams has attracted attention in recent years due
to its wide range of applications, including sensor networks, web clickstreams, and user …

Concept drift has been a very important concept in the realm of data streams. Streaming
data may consist of multiple drifting concepts each having its own underlying data …

Most current stream mining techniques can adapt to data distribution changes, known as
concept drift. Common concept drift detectors focus on detecting and signaling drift when a …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Data streams are significantly influenced by the notion change that is termed as concept
drift. The act of knowledge discovery from the data streams under notion adaption is a …

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 …

Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …

Abstract Concept drift represents that the underlying data generating distribution changes
over time and it is a common phenomenon in a stream of data sets. In particular, concept …