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== History ==
Data stream clustering has recently attracted attention for emerging applications that involve large amounts of streaming data. For clustering, [[k-means clustering | k-means]] is a widely used heuristic but alternate algorithms have also been developed such as [[k-Medoids]], [[CURE data clustering algorithm | CURE]] and the popular [[BIRCH(data clustering) | BIRCH]]. For data streams, one of the first results appeared in 1980 <ref>J.Munro and M. Paterson. [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4567985 Selection and Sorting with Limited Storage]. ''Theoretical Computer Science'', pages 315-323, 1980</ref> but the model was formalized in 1998 <ref>M. Henzinger, P. Raghavan, and S. Rajagopalan. [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.9554 Computing on Data Streams]. ''Digital Equipment Corporation, TR-1998-011'', August 1998.</ref>.
== Definition ==
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