The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient mining of constrained correlated sets
Abstract
Studies the problem of efficiently computing correlated item sets satisfying given constraints. We call them valid correlated item sets. It turns out that constraints can have subtle interactions with correlated item sets, depending on their underlying properties. We show that, in general, the set of minimal valid correlated item sets does not coincide with that of minimal correlated item sets that are valid, and we characterize classes of constraints for which these sets coincide. We delineate the meaning of these two spaces and give algorithms for computing them. We also give an analytical evaluation of their performance and validate our analysis with a detailed experimental evaluation.
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
Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
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
Constrained frequent pattern mining: a pattern-growth view
Pei, J., & Han, J. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/568574.568580 · Cited by 133
Metadata
- Authors
- Grahne, G., Lakshmanan, L. V. S., & Wang, X.
- Venue
- Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Pages
- 512-521
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W1888464276
- Cited by
- 162
- References
- 1
- FWCI
- 10.46
- Type
- conference paper
- 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: Efficient mining of constrained correlated sets. https://app.cochat.ai/papers/10.1109/icde.2000.839450
- The paper
- Grahne, G., Lakshmanan, L. V. S., & Wang, X. (2002). Efficient mining of constrained correlated sets. Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073). https://doi.org/10.1109/icde.2000.839450
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.
