The DOI matches its Crossref record.
A simple algorithm for finding frequent elements in streams and bags
Abstract
We present a simple, exact algorithm for identifying in a multiset the items with frequency more than a threshold θ. The algorithm requires two passes, linear time, and space 1/θ. The first pass is an on-line algorithm, generalizing a well-known algorithm for finding a majority element, for identifying a set of at most 1/θ items that includes, possibly among others, all items with frequency greater than θ.
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Citations
Cited by
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
Flipping bits in memory without accessing them: An experimental study of DRAM disturbance errors
Kim, Y., Daly, R., Kim, J., Fallin, C., Lee, J. H., Lee, D., Wilkerson, C., Lai, K., & Mutlu, O. (2014) · 2014 ACM/IEEE 41st International Symposium on Computer Architecture (ISCA) · doi:10.1109/isca.2014.6853210 · Cited by 465
Metadata
- Authors
- Karp, R. M., Shenker, S., & Papadimitriou, C. H.
- Venue
- ACM Transactions on Database Systems
- Published
- 2003-03
- Volume
- 28
- Issue
- 1
- Pages
- 51-55
- ISSN
- 0362-5915 (ISSN-L)
- 1557-4644
- OpenAlex
- W2113139394
- Cited by
- 491
- References
- 0
- FWCI
- 37.10
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A simple algorithm for finding frequent elements in streams and bags. https://app.cochat.ai/papers/10.1145/762471.762473
- The paper
- Karp, R. M., Shenker, S., & Papadimitriou, C. H. (2003). A simple algorithm for finding frequent elements in streams and bags. ACM Transactions on Database Systems. https://doi.org/10.1145/762471.762473
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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.
