Data stream clustering: Difference between revisions

Revision as of 23:29, 15 January 2010

Revision as of 18:45, 29 January 2010

Line 5:

== 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>. The method[[k-means clustering | K-means]] is a usuallywidely used heuristic for dataclustering streambut clusteringalso isalternate algorithms thehave been developed such as [[k-meansMedoids]], [[CURE data clustering algorithm | k-meansCURE]] and the popular [[BIRCH(data clustering) | BIRCH]].

== Algorithms ==