Stream mining is the process of discovering knowledge or patterns from continuous data streams. Unlike traditional data sets, data streams consist of sequences of data instances that flow in and out of a system continuously and with varying update rates. They are temporally ordered, fast changing, massive, and potentially infinite. Examples of data streams include data generated by communication networks, Internet traffic, online stock or business transactions, electric power grids, industry production processes, scientific and engineering experiments, and video, audio or remote sensing data from cameras, satellites, and sensor networks. Since it is usually impossible to store an entire data stream, or to scan through it multiple times due to its tremendous volume, most stream mining algorithms are confined to reading only once or a small number of times using limited computing and storage capabilities.