The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Random sampling with a reservoir
Open accessAbstract
We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records, where the value of N is unknown beforehand. The main result of the paper is the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in O ( n (1 + log( N/n ))) expected time, which is optimum, up to a constant factor. Several optimizations are studied that collectively improve the speed of the naive version of the algorithm by an order of magnitude. We give an efficient Pascal-like implementation that incorporates these modifications and that is suitable for general use. Theoretical and empirical results indicate that Algorithm Z outperforms current methods by a significant margin.
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Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,269
The Art of Computer Programming
Knuth, D. E. (1968) · Cited by 16,000
Proceedings. 45th Annual IEEE Symposium on Foundations of Computer Science
(2004) · 45th Annual IEEE Symposium on Foundations of Computer Science · doi:10.1109/focs.2004.4 · Cited by 805
Faster methods for random sampling
Vitter, J. S. (1984) · Communications of the ACM · doi:10.1145/358105.893 · Cited by 124
Self-citation
Cited by
Data Mining: Practical Machine Learning Tools and Techniques
(2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,437
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Practical Black-Box Attacks against Machine Learning
Papernot, N., McDaniel, P., Goodfellow, I., Jha, S., Celik, Z. B., & Swami, A. (2017) · Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security · doi:10.1145/3052973.3053009 · Cited by 3,495
CURE: an efficient clustering algorithm for large databases
Guha, S., Rastogi, R., & Shim, K. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276312 · Cited by 1,907
Metadata
- Authors
- Vitter, J. S.
- Venue
- ACM Transactions on Mathematical Software
- Published
- 1985-03
- Volume
- 11
- Issue
- 1
- Pages
- 37-57
- ISSN
- 0098-3500 (ISSN-L)
- 1557-7295
- OpenAlex
- W2119885577
- Cited by
- 1,758
- References
- 4
- FWCI
- 0.85
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Vitter, J. S. (1985). Random sampling with a reservoir. ACM Transactions on Mathematical Software. https://doi.org/10.1145/3147.3165
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
