Data stream clustering: Difference between revisions

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* [[Cobweb (clustering)|COBWEB]]:<ref>{{cite journal | first = D. H. | last = Fisher | url = http://link.springer.com/article/10.1023%2FA%3A1022852608280 | title = Knowledge Acquisition Via Incremental Conceptual Clustering | journal = Machine Learning | date = 1987 | doi=10.1023/A:1022852608280 | volume=2 | pages=139–172}}</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 }}</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 | url = http://dl.acm.org/citation.cfm?doid=130226.134466 | 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}}</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.
* [[AIS-Clus (Data Stream clustering)|AIS-Clus]]:<ref>{{cite journal | first = J. Salma | last = A. Amal| url = http://emcis2016.emcis.eu/EMCISfiles/EMCIS2016proceedings.pdf | title = Novelty Detection In Data Stream Clustering Using The Artificial Immune System | journal = Proceedings of the 13th European Mediterranean & Middle Eastern Conference on Information Systems (EMCIS) | date = 2016}}</ref> AIS-Clus is an adaptive algorithm which uses the strength of the Artificial Immune System meta-heuristic. Particularly, this algorithm uses the clonal selection theory to adapt clusters for the novel arriving textual streams and the negative selection process to detect novel concepts. AIS-Clus is a density based algorithm which can effectively handle the multi-class detection in the streaming scenario.
 
== References ==
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[[Category:Data clustering algorithms]]
 
 
*Wui Lee Chang, Kai Meng Tay, and Chee Peng Lim, A New Evolving Tree-Based Model with Local Re-learning for Document Clustering and Visualization, Neural Processing Letters, DOI: 10.1007/s11063-017-9597-3. https://link.springer.com/article/10.1007/s11063-017-9597-3