International Journal of Computer Applications
Data stream mining is the process of extracting knowledge structures from continuous, rapid data records. In these applications, the main goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. In many applications which are in non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e., the class or the target value to be predicted may change over time.