Data stream clustering: Difference between revisions

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Other well-known algorithms used for data stream clustering are:

* [[BIRCH (data clustering)|BIRCH]]:<ref name="birch">{{cite journal | first1 = T. | last1 = Zhang | first2 = R. | last2 = Ramakrishnan | first3 = M. | last3 = Linvy | title = BIRCH: An efficient data clustering method for very large databases | journal = ACM SigmodSIGMOD Record |doi=10.1145/235968.233324 | date = 1996 | volume=25 | issue = 2 | pages=103–114| doi-access = free }}</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 {{tmath|O(N)}} since one pass suffices to get a good clustering (though, results can be improved by allowing several passes).

* [[Cobweb (clustering)|COBWEB]]:<ref>{{cite journal | first = D. H. | last = Fisher | title = Knowledge Acquisition Via Incremental Conceptual Clustering | journal = Machine Learning | date = 1987 | doi=10.1023/A:1022852608280 | volume=2 | issue = 2 | pages=139–172| doi-access = free }}</ref><ref>{{cite journal | first = D. H. | last = Fisher | citeseerx = 10.1.1.6.9914 | title = Iterative Optimization and Simplification of Hierarchical Clusterings | journal = Journal of AI Research | volume = 4 | date = 1996 | arxiv = cs/9604103 | bibcode = 1996cs........4103F }}</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]]).

* [[C2ICM(incremental clustering)|C2ICM]]:<ref>{{cite journal | first = F. | last = Can | title = Incremental Clustering for Dynamic Information Processing | journal = ACM Transactions on Information Systems | volume = 11 | issue = 2 | date = 1993 | pages = 143–164 | doi=10.1145/130226.134466| s2cid = 1691726 | doi-access = free }}</ref> builds a flat partitioning clustering structure by selecting some objects as cluster seeds/initiators and a non-seed is assigned to the seed that provides the highest coverage, addition of new objects can introduce new seeds and falsify some existing old seeds, during incremental clustering new objects and the members of the falsified clusters are assigned to one of the existing new/old seeds.