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 …

Learning non-stationary data streams is challenging due to the unique characteristics of
infinite length and evolving property. Current existing works often concentrate on 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 …

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 …

Streaming data is one of the attention receiving sources for concept-evolution studies. When
a new class occurs in the data stream it can be considered as a new concept and so the …

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 …

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 …

Data stream mining has gained increasing attention in recent years due to its wide range of
applications. In this paper, we propose a new selective prototype-based learning (SPL) …

Most of the approaches for classifying evolving data stream divide the stream into fixed size
chunks to address infinite length and concept drift problems. These approaches suffer from …

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 …