In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between tw...
Classes issued of evolving systems are dynamic and their characteristics vary over the time. Assigning a pattern to a class is achieved using a classifier. Therefore, the classifie...
A Bayesian ensemble learning method is introduced for unsupervised extraction of dynamic processes from noisy data. The data are assumed to be generated by an unknown nonlinear ma...
The two main challenges typically associated with mining data streams are concept drift and data contamination. To address these challenges, we seek learning techniques and models ...
Clustering ensembles combine different clustering solutions into a single robust and stable one. Most of existing methods become highly time-consuming when the data size turns to ...