Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,751

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Scalable algorithms for association mining

Zaki, M.J.·2000·IEEE Transactions on Knowledge and Data Engineering
DOI 10.1109/69.846291
Open this paper in CoChat — free account

Abstract

Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. We present efficient algorithms for the discovery of frequent itemsets which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sublattices, which can be solved in memory. Efficient lattice traversal techniques are presented which quickly identify all the long frequent itemsets and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/69.846291

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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

Show all 2 references

Research works printed but not linked: 27

  1. 10.1023/A:1009773317876 · DOI · Paper page
  2. zaki, 1997, new algorithms for fast discovery of association rules, Third Int l Conf Knowledge Discovery and Data Mining, 10.1023/A:1009773317876 · DOI · Paper page
  3. gunopulos, 1997, discovering all the most specific sentences by randomized algorithms, Int l Conf Database Theory
  4. gunopulos, 1997, data mining, hypergraph transversals, and machine learning, Proc 16th ACM Symp Principles of Database Systems
  5. garey, 1979, Computers and Intractability A Guide to the Theory of NP-Completeness
  6. 10.1016/0196-6774(92)90012-2 · doi:10.1016/0196-6774(92)90012-2
  7. 10.1145/253260.253330 · DOI · Paper page
  8. holsheimer, 1995, a perspective on databases and data mining, First Int l Conf Knowledge Discovery and Data Mining
  9. lin, 1998, pincer-search: a new algorithm for discovering the maximum frequent set, Sixth Int l Conf Extending Database Technology
  10. kuznetsov, 1989, interpretation on graphs and complexity characteristics of a search for specific patterns, Nauchn Tekh Inf Ser 2 (Automatic Document Math Linguist), 23, 23
  11. 10.1109/ICDE.1998.655811 · doi:10.1109/icde.1998.655811
  12. 10.1145/321694.321698 · doi:10.1145/321694.321698
  13. 10.1109/69.634757 · DOI · Paper page
  14. 10.1145/253260.253325 · DOI · Paper page
  15. 10.1109/PDIS.1996.568665 · DOI · Paper page
  16. davey, 1990, Introduction to Lattices and Order
  17. 10.1016/0020-0190(94)90121-X · DOI
  18. agrawal, 1996, fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, 307
  19. 10.1109/RIDE.1997.583696 · DOI
  20. 10.1109/69.553164 · DOI · Paper page
  21. yen, 1996, an efficient approach to discovering knowledge from large databases, Fourth Int l Conf Parallel and Distributed Information Systems
  22. agrawal, 1994, fast algorithms for mining association rules, Proc 20th Very Large Data Base Conf
  23. 10.1145/276304.276313 · DOI · Paper page
  24. sarawagi, 1998, integrating association rule mining with databases: alternatives and implications, ACM SIGMOD Int l Conf Management of Data, 10.1145/276305.276335 · doi:10.1145/276305.276335
  25. parthasarathy, 1998, memory placement techniques for parallel association mining, Fourth Int l Conf Knowledge Discovery and Data Mining
  26. toivonen, 1996, sampling large databases for association rules, Proc 22nd Very Large Data Bases Conf
  27. savasere, 1995, an efficient algorithm for mining association rules in large databases, Proc 21st Very Large Data Bases Conf

Cited by

See all 1,751

Metadata

Authors
Zaki, M. J.
Venue
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2000
Volume
12
Issue
3
Pages
372-390
ISSN
1041-4347 (ISSN-L)
DOI
10.1109/69.846291
OpenAlex
W2099404336
Cited by
1,751
References
2
FWCI
56.95
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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: Scalable algorithms for association mining. https://app.cochat.ai/papers/10.1109/69.846291
The paper
Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/69.846291

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.