The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
arules- A Computational Environment for Mining Association Rules and Frequent Item Sets
Open accessAbstract
Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and 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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
The Art of Computer Programming: Volume 3: Sorting and Searching
Knuth, D. E. (1973) · Cited by 3,955
UCI Repository of Machine Learning Databases
Merz, C. J. (1996) · Medical Entomology and Zoology · Cited by 2,363
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
Scalable algorithms for association mining
Zaki, M. J. (2000) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.846291 · Cited by 1,751
Knowledge Discovery in Databases
Piateski, G., & Frawley, W. (1991) · The MIT Press eBooks · Cited by 1,749
Cited by
Open-source machine learning: R meets Weka
Hornik, K., Buchta, C., & Zeileis, A. (2009) · Computational Statistics · doi:10.1007/s00180-008-0119-7 · Cited by 385
Self-citationNoguchi, Y., Tachi, T., & Teramachi, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab347 · Cited by 285
A Taxonomy of Evaluation Methods for Information Systems Artifacts
Prat, N., Comyn-Wattiau, I., & Akoka, J. (2015) · Journal of Management Information Systems · doi:10.1080/07421222.2015.1099390 · Cited by 278
Gao, Y., Xu, A., Hu, P. J.-H., & Cheng, T.-H. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.01.007 · Cited by 26
An extended transcription factor regulatory network controls hepatocyte identity
Dubois‐Chevalier, J., Gheeraert, C., Berthier, A., Boulet, C., Dubois, V., Guille, L., Fourcot, M., Marot, G., Gauthier, K., Dubuquoy, L., et al. (2023) · EMBO Reports · doi:10.15252/embr.202357020 · Cited by 6
Metadata
- Authors
- Hahsler, M., Grün, B., & Hornik, K.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2005
- Volume
- 14
- Issue
- 15
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2133610185
- Cited by
- 552
- References
- 32
- FWCI
- 12.57
- Type
- journal article
- Language
- en
- Open access
- diamond
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: arules- A Computational Environment for Mining Association Rules and Frequent Item Sets. https://app.cochat.ai/papers/10.18637/jss.v014.i15
- The paper
- Hahsler, M., Grün, B., & Hornik, K. (2005). arules- A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software. https://doi.org/10.18637/jss.v014.i15
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.
