Data Stream Mining

Abstract

Data Stream Mining is the process of extracting knowledge structures from continuous , rapid data records. A data stream is a sequence of unbounded, real time data items with a very high data rate. Examples of data streams include computer network traffic, web searches, phone conversations, ATM transactions and sensor data. The various data mining techniques discussing are Sampling, Load Shedding and Clustering. Sampling involves finding uniform random samples of an infinite data stream. Load Shedding is the process of dropping a sequence of data items. Clustering is the process of organizing objects into groups whose members are similar in some way