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Other well-known algorithms used for data stream clustering are:
* [[BIRCH (data clustering) | BIRCH]]<ref>T. Zhang, R. Ramakrishnan, M. Linvy. [http://doi.acm.org/10.1145/235968.233324 BIRCH: An Efficient Data Clustering Method for Very Large Databases], Proceedings of the ACM SIGMOD Conference on Management of Data, 1996</ref> : builds a hierarchical data structure to incrementally cluster the incoming points using the available memory and minimizing the amount of I/O required. The complexity of the algorithm is ''O(N)'' since one pass suffices to get a good clustering (though, results can be improved by allowing several passes).
* [[Cobweb (clustering) | COBWEB]]<ref>D.H. Fisher [http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.6.9914 Iterative Optimization and Simplification of Hierarchical Clusterings]. Journal of AI Research, Vol 4, 1996 </ref> : is an incremental clustering technique that keeps a hierarchical clustering model in the form of a [[ Decision tree learning | classification tree]]. For each new point. COBWEB descends the tree, updates the nodes along the way and looks for the best node to put the point on (using a [[Category utility | category utility function]]).
== References ==
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