Due to the unprecedented speed and volume of generated raw data in most of applications,
data stream mining has attracted a lot of attention recently. Methods for solving these …

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 …

Classification plays a crucial role in mining the evolving data streams. The concept drift and
concept evolution are the major issues of data streams classification, which greatly affect the …

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 is one of the most challenging areas in the machine learning. In
this paper, we focus on three major challenges namely infinite length, concept-drift and …

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 …

Data streams have some unique properties which make them applicable in precise
modeling of many real data mining applications. The most challenging property of data …

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 …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

Data stream mining has gained growing attentions due to its wide emerging applications
such as target marketing, email filtering and network intrusion detection. In this paper, we …