Mining association rules between sets of items in large databases
Open accessAbstract
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
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
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.
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?
In a rule-based system, the traditional association rule learning technique may produce redundant rules from the data that makes the decision-making process complex and ineffective.
“For instance, in a rule-based system, the traditional association rule learning technique [4] may produce redundant rules from the data that makes the decision-making process complex and ineffective [113].”
From Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective (2021) · cites this paper as [4]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279 · see citing works
Flagged first, then by citations
Research works printed but not linked: 7
- Swami, "Database Mining: A Performance Per- spective", IEEE Transactions on Knowledge and Data Engineering, Special Issue on Learning and Discovery in Knowledge-Based Databases, (to ap- pear).
- Rakesh Agrawal, Sakti Ghosh, Tomasz Imielinski, Bala Iyer, and Arun Swami, "An Interval Classifier for Database Mining Applications", VLDB-92, Vancouver, British Columbia, 1992, 560-573.
- B. Falkenhainer and R. Michalski, "Integrat- ing Quantitative and Qualitative Discovery: The ABACUS System", Machine Learning, 1(4): 367- 401.
- M. Kokar, "Discovering Functional Formulas through Changing Representation Base", Proceed- ings of the Fifih National Conference on Artificial Intelligence, 1986,455-459.
- P. Langley, H. Simon, G. Bradshaw, and J. Zytkow, Scientific Discovery: Computational Explorations of the Creative Process, The MIT Press, Cam- bridge, Mass., 1987.
- G. Piatetsky-Shapiro (Editor), Knowledge Discov- ery in Databases, AAAI/MIT Press, 1991.
- L.G. Valiant, "Learning Disjunctions and Conjunc- tions", IJCAI-85, Los Angeles, 1985, 560-565.
Cited by
Sorted by citations
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583 · see citing works
“Association rule learning is a rule-based machine learning approach to discover interesting relationships, “IF-THEN” statements, in large datasets between variables [7].”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332 · see citing works
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014) · Journal of Complex Networks · doi:10.1093/comnet/cnu016 · Cited by 3,235 · see citing works
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445 · see citing works
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works
Privacy preserving association rule mining in vertically partitioned data
Vaidya, J., & Clifton, C. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775142 · Cited by 1,001 · see citing works
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818 · see citing works
The digital revolution in financial inclusion: international development in the fintech era
Gabor, D., & Brooks, S. (2017) · New Political Economy · doi:10.1080/13563467.2017.1259298 · Cited by 796 · see citing works
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766 · see citing works
Geospatial big data handling theory and methods: A review and research challenges
Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., et al. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2015.10.012 · Cited by 749 · see citing works
Interpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687 · see citing works
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620 · see citing works
<scp>B</scp> ig <scp>D</scp> ata and analytics in higher education: Opportunities and challenges
Daniel, B. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12230 · Cited by 610 · see citing works
Loyola-Gonzalez, O. (2019) · IEEE Access · doi:10.1109/access.2019.2949286 · Cited by 559 · see citing works
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570 · see citing works
Association rule mining to detect factors which contribute to heart disease in males and females
Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.08.028 · Cited by 394 · see citing works
Query expansion techniques for information retrieval: a survey
Azad, H. K., & Deepak, A. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.05.009 · Cited by 313 · see citing works
Trust building with explanation interfaces
Pu, P., & Chen, L. (2006) · Proceedings of the 11th international conference on Intelligent user interfaces · doi:10.1145/1111449.1111475 · Cited by 300 · see citing works
Set-oriented mining for association rules in relational databases
Houtsma, M., & Swami, A. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380413 · Cited by 263 · see citing works
Self-citationConstructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287 · see citing works
“From the KB, there are several approaches that mine association rules (Agrawal et al., 1993) and several mine logical rules (Schoenmackers et al.”
BackgroundHopfield Networks is All You Need
Ramsauer, H., Schäfl, B., Lehner, J., Seidl, P., Widrich, M., Adler, T., Gruber, L., Holzleitner, M., Pavlović, M., Sandve, G. K., et al. (2020) · arXiv · doi:10.48550/arxiv.2008.02217 · Cited by 236 · see citing works
“The Hopfield layer in a deep neural network architecture can implement: • a memory (storage) with associative retrieval [20, 5], • conditional pooling and averaging operations [67, 39], • combining data by associations [2], • associative credit assignment (e.”
BackgroundMobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions
Sarker, I. H., Hoque, M. M., Uddin, M. K., & Alsanoosy, T. (2021) · Mobile Networks and Applications · doi:10.1007/s11036-020-01650-z · Cited by 207 · see citing works
Mining query logs: turning search usage data into knowledge
Silvestri, F. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000013 · Cited by 194 · see citing works
Anonymizing transaction databases for publication
Xu, Y., Wang, K., Fu, A. W.-C., & Yu, P. S. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401982 · Cited by 187 · see citing works
A comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164 · see citing works
Big Data Analysis-Based Security Situational Awareness for Smart Grid
Wu, J., Ota, K., Dong, M., Li, J., & Wang, H. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2616146 · Cited by 144 · see citing works
Intelligent assistance for teachers in collaborative e-learning environments
Casamayor, A., Amandi, A., & Campo, M. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.05.025 · Cited by 106 · see citing works
Zuo, Y., Wu, Y., Min, G., Huang, C., & Pei, K. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2966615 · Cited by 94 · see citing works
Sarker, I. H., & Kayes, A. S. M. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102762 · Cited by 75 · see citing works
Prognostication and risk factors for cystic fibrosis via automated machine learning
Alaa, A. M., & van der Schaar, M. (2018) · Scientific Reports · doi:10.1038/s41598-018-29523-2 · Cited by 80 · see citing works
RecencyMiner: mining recency-based personalized behavior from contextual smartphone data
Sarker, I. H., Colman, A., & Han, J. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0211-6 · Cited by 65 · see citing works
Arsevska, E., Valentin, S., Rabatel, J., de Goër de Hervé, J., Falala, S., Lancelot, R., & Roche, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199960 · Cited by 64 · see citing works
On the credibility perception of news on Twitter: Readers, topics and features
Shariff, S. M., Zhang, X., & Sanderson, M. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.06.026 · Cited by 49 · see citing works
Xia, Q., Li, W., Yang, Y., Weng, X., & Chiu, T. K. F. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100455 · Cited by 45 · see citing works
Kowatsch, T., Lohse, K.-M., Erb, V., Schittenhelm, L., Galliker, H., Lehner, R., & Huang, E. M. (2021) · Journal of Medical Internet Research · doi:10.2196/23612 · Cited by 39 · see citing works
Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce
Paraschakis, D. (2018) · doi:10.24834/2043/24268 · Cited by 35 · see citing works
Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms
Hossain, M. M., Sun, X., Mitran, E., & Rahman, M. A. (2021) · IATSS Research · doi:10.1016/j.iatssr.2021.07.002 · Cited by 26 · see citing works
Cheng, X., Wang, Z., Yang, X., Xu, L., & Liu, Y. (2021) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101627 · Cited by 22 · see citing works
Learning Strength and Weakness Rules of Cricket Players Using Association Rule Mining
Behera, S. R., & Saradhi, V. V. (2022) · Communications in Computer and Information Science · doi:10.1007/978-3-031-02044-5_7 · Cited by 4 · see citing works
BUILDING USER TASK SPACE BY MINING TEMPORALLY PROXIMATE DESKTOP ACTIONS
Mirza, H. T., Chen, L., Majid, A., & Chen, G. (2011) · Cybernetics and Systems · doi:10.1080/01969722.2011.634675 · Cited by 2 · see citing works
- Authors
- Agrawal, R., Imieliński, T., & Swami, A.
- Affiliations
- IBM Research - Almaden (US)
- Rutgers, The State University of New Jersey (US)
- Venue
- Proceedings of the 1993 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 1993-06
- Pages
- 207-216
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2166559705
- Cited by
- 14,907
- References
- 2
- Citations by year
- 2026: 140 · 2025: 231 · 2024: 223 · 2023: 271 · 2022: 304 · 2021: 486 · 2020: 514 · 2019: 560 · 2018: 617 · 2017: 653 · 2016: 644 · 2015: 714 · 2014: 832 · 2013: 803 · 2012: 863
- FWCI
- 39.75
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGMOD International Conference on Management of Data (SIGMOD) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- association rule mining, frequent itemset mining, transactional databases, buffer management, pruning techniques, estimation methods, retail sales data, consumer behavior
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
