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== History ==
The problem of data stream clustering has recently attracted much attention for its applicability to emerging applications that involve 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>. [[k-means clustering | K-means]] is a widely used heuristic for clustering but also alternate algorithms for clustering have been developed such as [[k-Medoids]], [[CURE data clustering algorithm | CURE]] and the popular [[BIRCH(data clustering) | BIRCH]].
== Algorithms ==
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