The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and ...

The current ability to produce massive amounts of data and the impossibility in storing it motivated the development of data stream mining strategies. Despite the proposal of many techniques, this research area still lacks ...

Learning from continuous streams of data has been receiving an increasingly attention in the last years. Among the many challenges related to mining data streams, change detection is one topic frequently addressed. Being ...

The detection of concept drift allows to point out when a data stream changes its behavior over time, what supports further analysis to understand why the phenomenon represented by such data has changed. Nowadays, researchers ...

Given a data stream with many attributes and high frequency of events, how to cluster similar events? Can it be done in real time? For example, how to cluster decades of frequent measurements of tens of climatic attributes ...

The ability to detect changes in the data distribution is an important issue in Data Stream mining. Detecting changes in data distribution allows the adaptation of a previously learned model to accommodate the most recent ...

Data stream mining is an emergent research area that investigates knowledge extraction from large amounts of continuously generated data, produced by non-stationary distribution. Novelty detection, the ability to identify ...