Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

Most existing data stream classification techniques ignore one important aspect of stream
data: arrival of a novel class. We address this issue and propose a data stream classification …

Class evolution, the phenomenon of class emergence and disappearance, is an important
research topic for data stream mining. All previous studies implicitly regard class evolution …

In recent years, a variety of research areas have contributed to a set of related problems with
rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple …

Data stream classification poses many challenges to the data mining community. In this
paper, we address four such major challenges, namely, infinite length, concept-drift, concept …

Recent approaches for classifying data streams are mostly based on supervised learning
algorithms, which can only be trained with labeled data. Manual labeling of data is both …

The problem of data stream classification is challenging because of many practical aspects
associated with efficient processing and temporal behavior of the stream. Two such well …

Mining data streams has been a focal point of research interest over the past decade.
Hardware and software advances have contributed to the significance of this area of …