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Better streaming algorithms for clustering problems
Abstract
We study clustering problems in the streaming model, where the goal is to cluster a set of points by making one pass (or a few passes) over the data using a small amount of storage space. Our main result is a randomized algorithm for the k--Median problem which produces a constant factor approximation in one pass using storage space O(k poly log n). This is a significant improvement of the previous best algorithm which yielded a 2O(1/ε) approximation using O(nε) space. Next we give a streaming algorithm for the k--Median problem with an arbitrary distance function. We also study algorithms for clustering problems with outliers in the streaming model. Here, we give bicriterion guarantees, producing constant factor approximations by increasing the allowed fraction of outliers slightly.
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Citations
Cited by
(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
Density-Based Clustering over an Evolving Data Stream with Noise
Cao, F., Estert, M., Qian, W., & Zhou, A. (2006) · Proceedings of the 2006 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972764.29 · Cited by 1,016
Social big data: recent achievements and new challenges
Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846
“A new generation of online [132, 133] and streaming [134, 135] algorithms is currently being developed in order to manage social big data challenges, and these algorithms require high scalability in both memory consumption [136] and time computation.”
MethodData Streams: Algorithms and Applications
Muthukrishnan, S. (2005) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000002 · Cited by 718
Data stream clustering: A survey
Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., Carvalho, A. C. P. L. F. D., & Gama, J. (2013) · ACM Computing Surveys · doi:10.1145/2522968.2522981 · Cited by 508
Metadata
- Authors
- Charikar, M., O'Callaghan, L., & Panigrahy, R.
- Venue
- Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2003-06-09
- Pages
- 30-39
- OpenAlex
- W2091684877
- Cited by
- 230
- References
- 0
- FWCI
- 10.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Charikar, M., O'Callaghan, L., & Panigrahy, R. (2003). Better streaming algorithms for clustering problems. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing. https://doi.org/10.1145/780542.780548
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