Data stream clustering: Difference between revisions

Revision as of 22:02, 12 January 2010

Revision as of 00:27, 14 January 2010

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== History ==

The problem of data stream clustering has recently attracted much attention for its applicability to emerging applications that involve a large

amount of streaming data such as network flows, sensor data, and web click streams. One of the first results on data streams was due to Munro and Paterson <ref>J.Munro and M. Paterson. Selection and Sorting with Limited Storage. ''Theoretical Computer Science'', pages 315-323, 1980</ref> but the model was formalized much later by Henzinger, Raghavan, and Rajagopalan <ref>M. Henzinger, P. Raghavan, and S. Rajagopalan. ''Computing on Data Streams. Digital Equipment Corporation, TR-1998-011'', August 1998.</ref>. The method used for data stream clustering is the [[k-means clustering | k-means]]

Data..

== Algorithms ==

Many algorithms have been proposed for the data stream clustering problem. One of the basic requisites is that the computation must be carried out in small space.

== References ==

<references> <references/>

== Notes ==

http://www.cc.gatech.edu/projects/disl/Courses/cs4440/07Fall/project/proposals/Team5Proposal_final.pdf