Data stream clustering: Difference between revisions

Revision as of 20:12, 29 January 2023

Revision as of 17:30, 22 February 2023

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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{{citation needed|date=July 2017}} [[BIRCH (data clustering)|BIRCH]]. For data streams, one of the first results appeared in 1980<ref>{{cite journal | first1 = J. | last1 = Munro | first2 = M. | last2 = Paterson | url = https://ieeexplore.ieee.org/document/4567985 | title = Selection and Sorting with Limited Storage | journal = Theoretical Computer Science | volume = 12 | issue = 3 | pages = 315–323 | date = 1980 | doi = 10.1016/0304-3975(80)90061-4 | doi-access = free }}</ref> but the model was formalized in 1998.<ref>{{cite journal | first1 = M. | last1 = Henzinger | first2 = P. | last2 = Raghavan | first3 = S. | last3 = Rajagopalan | citeseerx = 10.1.1.19.9554 | title = Computing on Data Streams | journal = Digital Equipment Corporation | volume = TR-1998-011 | date = August 1998 }}</ref>

== Definition ==