The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Pincer-search: A new algorithm for discovering the maximum frequent set
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 in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
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
Mining generalized association rules
Srikant, R., & Agrawal, R. (1997) · Future Generation Computer Systems · doi:10.1016/s0167-739x(97)00019-8 · Cited by 1,618
An Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,590
Mitchell, T. M. (1982) · Artificial Intelligence · doi:10.1016/0004-3702(82)90040-6 · Cited by 1,412
An effective hash-based algorithm for mining association rules
Park, J. S., Chen, M.-S., & Yu, P. S. (1995) · Proceedings of the 1995 ACM SIGMOD international conference on Management of data - SIGMOD '95 · doi:10.1145/223784.223813 · Cited by 1,406
New algorithms for fast discovery of association rules
Zaki, M. J., Parthasarathy, S., Ogihara, M., & Wei, L. (1997) · Cited by 1,126
Sampling Large Databases for Association Rules
Toivonen, H. T. T. (1996) · Cited by 1,089
Parallel mining of association rules
Agrawal, R., & Shafer, J. C. (1996) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.553164 · Cited by 1,064
Discovery, Analysis, and Presentation of Strong Rules
Piatetsky-Shapiro, G. (1991) · Medical Entomology and Zoology · Cited by 993
Discovery of Multiple-Level Association Rules from Large Databases
Han, J., & Fu, Y. (1995) · Cited by 922
Levelwise Search and Borders of Theories in Knowledge Discovery
Mannila, H., & Toivonen, H. T. T. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009796218281 · Cited by 889
Discovering Frequent Episodes in Sequences.
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1995) · Knowledge Discovery and Data Mining · Cited by 590
Özden, B., Ramaswamy, S., & Silberschatz, A. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.1998.655804 · Cited by 411
Parallel Algorithms for Discovery of Association Rules
Zaki, M. J., Parthasarathy, S., Ogihara, M., Li, W., Stolorz, P., & Musick, R. (1997) · Scalable High Performance Computing for Knowledge Discovery and Data Mining · doi:10.1007/978-1-4615-5669-5_1 · Cited by 264
Discovering all most specific sentences by randomized algorithms extended abstract
Gunopulos, D., Mannila, H., & Saluja, S. (1997) · Lecture notes in computer science · doi:10.1007/3-540-62222-5_47 · Cited by 31
Cited by
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
Discovering Frequent Closed Itemsets for Association Rules
Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_25 · Cited by 1,378
Metadata
- Authors
- Lin, D.-I., & Kedem, Z. M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 1998
- Pages
- 103-119
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1482472192
- Cited by
- 277
- References
- 17
- FWCI
- 13.68
- 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
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.
