Data with temporal information is constantly generated, sampled, gathered, and analyzed in different domains, such as medicine, finance, engineering, environmental sciences, and e...
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Abstract- Data streams of real numbers are generated naturally in many applications. The technology of online subsequence searching in data streams becomes more and more important ...
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be c...
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at cons...