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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3616

Title: Data Stream Mining
Authors: Jyothis Joseph
Keywords: Data stream mining, Data Stream Management System, Reservoir Sampling, Load Shedding , Clustering
Issue Date: 22-Jun-2011
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
URI: http://hdl.handle.net/123456789/3616
Appears in Collections:MTech 2010-2012 Batch

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