Stream data is usually in vast volume, changing dynamically, possibly infinite, and
containing multi-dimensional features. The attention towards data stream mining is …

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 …

Data stream mining has taken over as a new field of research during past few years. It has
gained lot of attention recently due to its challenging characteristics like dynamic nature …

This paper proposes an online data stream classification that learns with limited labels using
selective self-training. Data partitioning steps are proposed to improve stream mining …

We present ActMiner, which addresses four major challenges to data stream classification,
namely, infinite length, concept-drift, concept-evolution, and limited labeled data. Most of the …

Mining data streams such as Internet traffic and network security is complex. Due to the
difficulty of storage, data streams analytics need to be done in one scan. This limits the time …

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 …

Data stream mining refers to methods able to mine continuously arriving and evolving data
sequences or even large scale static databases. Mining data streams has attracted much …

Increasing access to large-scale, high-dimensional and non-stationary streams in many real
applications has made it necessary to design new dynamic classification algorithms. Most …

Data streams gained obvious attention by researches for years. Mining this type of data
generates challenges because of their special nature. Classification is one of the major …