Stream Data Classification and Adapting to Gradual Concept Drift
Stream data are sequence of data examples that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques (e.g., sensor readings, call records, web page visits). Classification techniques fail to successfully process data streams because of two factors: their overwhelming volume and their distinctive feature known as concept drift. Concept drift is defined as changes in the learned structure that occur over time. The occurrence of concept drift leads to a drastic drop in classification accuracy.