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41st Annual Symposium on Foundations of Computer Science
Clustering data streams
Redondo Beach, California
November 12November 14
ISBN: 0769508502
ASCII Text  x  
S. Guha, N. Mishra, R. Motwani, L. O'Callaghan, "Clustering data streams," 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp. 359, 41st Annual Symposium on Foundations of Computer Science, 2000.  
BibTex  x  
@article{ 10.1109/SFCS.2000.892124, author = {S. Guha and N. Mishra and R. Motwani and L. O'Callaghan}, title = {Clustering data streams}, journal ={2013 IEEE 54th Annual Symposium on Foundations of Computer Science}, volume = {0}, year = {2000}, issn = {02725428}, pages = {359}, doi = {http://doi.ieeecomputersociety.org/10.1109/SFCS.2000.892124}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  CONF JO  2013 IEEE 54th Annual Symposium on Foundations of Computer Science TI  Clustering data streams SN  02725428 SP EP A1  S. Guha, A1  N. Mishra, A1  R. Motwani, A1  L. O'Callaghan, PY  2000 KW  data analysis; pattern clustering; very large databases; computational complexity; deterministic algorithms; data stream clustering; point sequence; data stream model; massive data sets; Web click stream analysis; multimedia data analysis; constantfactor approximation algorithms; kmedian problem; deterministic algorithms VL  0 JA  2013 IEEE 54th Annual Symposium on Foundations of Computer Science ER   
We study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model is relevant to new classes of applications involving massive data sets, such as Web click stream analysis and multimedia data analysis. We give constantfactor approximation algorithms for the kmedian problem in the data stream model of computation in a single pass. We also show negative results implying that our algorithms cannot be improved in a certain sense.
Index Terms:
data analysis; pattern clustering; very large databases; computational complexity; deterministic algorithms; data stream clustering; point sequence; data stream model; massive data sets; Web click stream analysis; multimedia data analysis; constantfactor approximation algorithms; kmedian problem; deterministic algorithms
Citation:
S. Guha, N. Mishra, R. Motwani, L. O'Callaghan, "Clustering data streams," focs, pp.359, 41st Annual Symposium on Foundations of Computer Science, 2000
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