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 …

In a typical data stream classification task, it is assumed that the total number of classes are
fixed. This assumption may not be valid in a real streaming environment, where new classes …

Data stream classification poses many challenges, most of which are not addressed by the
state-of-the-art. We present DXMiner, which addresses four major challenges to data stream …

Concept-evolution is one of the major challenges in data stream classification, which occurs
when a new class evolves in the stream. This problem remains unaddressed by most state …

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 …

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 …

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 …

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 …

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 …

Concept-evolution has recently received a lot of attention in the context of mining data
streams. Concept-evolution occurs when a new class evolves in the stream. Although many …