The DOI matches its Crossref record.
Knowledge Discovery from Data Streams
Abstract
Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Predicting Taxi–Passenger Demand Using Streaming Data
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L. (2013) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2013.2262376 · Cited by 721
Self-citationMining big data: current status, and forecast to the future
Fan, W., & Bifet, A. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481246 · Cited by 677
Fekr, A. R., Radecka, K., & Zilic, Z. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2445783 · Cited by 61
Soto, J. A. C., Jentsch, M., Preuveneers, D., & Ilie-Zudor, E. (2016) · Proceedings of the 6th International Conference on the Internet of Things · doi:10.1145/2991561.2991575 · Cited by 28
Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection
Peccatiello, R. B., Gondim, J. J. C., & Garcia, L. P. F. (2023) · IEEE Access · doi:10.1109/access.2023.3293825 · Cited by 25
Metadata
- Authors
- Gama, J.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2010-05-25
- OpenAlex
- W1537430301
- Cited by
- 571
- References
- 0
- FWCI
- 10.77
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Knowledge Discovery from Data Streams. https://app.cochat.ai/papers/10.1201/ebk1439826119
- The paper
- Gama, J. (2010). Knowledge Discovery from Data Streams. Chapman & Hall/CRC eBooks. https://doi.org/10.1201/ebk1439826119
Data sources
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.
