Mining data streams of changing class distributions is important for real-time business
decision support. The stream classifier must evolve to reflect the current class distribution …

Recently, mining data streams with concept drifts for actionable insights has become an
important and challenging task for a wide range of applications including credit card fraud …

Knowledge discovery from infinite data streams is an important and difficult task. We are
facing two challenges, the overwhelming volume and the concept drifts of the streaming …

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

Recent years have witnessed a large body of research work on mining concept drifting data
streams, where a primary assumption is that the up-to-date data chunk and the yet-to-come …

Mining concept drifting data streams is a defining challenge for data mining research.
Recent years have seen a large body of work on detecting changes and building prediction …

We demonstrate StreamMiner, a random decision-tree ensemble based engine to mine data
streams. A fundamental challenge in data stream mining applications (eg, credit card …

Many applications track streaming data for actionable alerts, which may include, for
example, network intrusions, transaction frauds, bio-surveilence abnormalities, and so forth …

Detecting concept drifts and reducing the impact from the noise in real applications of data
streams are challenging but valuable for inductive learning. It is especially a challenge in a …