The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Discovering Frequent Closed Itemsets for Association Rules
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
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
Introduction to Lattices and Order
Davey, B. A., & Priestley, H. A. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809088 · Cited by 4,901
Data mining and knowledge discovery: making sense out of data
Feyyad, U. M. (1996) · IEEE Expert · doi:10.1109/64.539013 · Cited by 4,612
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Data mining: an overview from a database perspective
Chen, M.-S., Han, J., & Yu, P. S. (1996) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.553155 · Cited by 2,232
Dynamic itemset counting and implication rules for market basket data
Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997) · Proceedings of the 1997 ACM SIGMOD international conference on Management of data - SIGMOD '97 · doi:10.1145/253260.253325 · Cited by 1,951
An Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,590
Efficiently mining long patterns from databases
Bayardo, R. J. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276313 · Cited by 1,302
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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
Business intelligence: data mining and optimization for decision making
Vercellis, C., Ahmadi, A., & Mohebi, A. (2016) · doi:10.5281/zenodo.13983826 · Cited by 398
FP-outlier: Frequent pattern based outlier detection
He, Z., Xu, X., Huang, Z., & Deng, S. (2005) · Computer Science and Information Systems · doi:10.2298/csis0501103h · Cited by 188
Discrimination- and privacy-aware patterns
Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., & Giannotti, F. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0393-7 · Cited by 93
Metadata
- Authors
- Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1999
- Pages
- 398-416
- ISSN
- 0302-9743 (ISSN-L)
- OpenAlex
- W1585646276
- Cited by
- 1,378
- References
- 20
- FWCI
- 19.53
- 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: Discovering Frequent Closed Itemsets for Association Rules. https://app.cochat.ai/papers/10.1007/3-540-49257-7_25
- The paper
- Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Discovering Frequent Closed Itemsets for Association Rules. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-49257-7_25
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.
