research-article

Mining Multiple-Level Association Rules in Large Databases

Authors:

Yongjian FuAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 11, Issue 5

Pages 798 - 805

https://doi.org/10.1109/69.806937

Published: 01 September 1999 Publication History

Abstract

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding level-crossing association rules, are also investigated in the paper. Our study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

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Cited By

Zhu X(2022)Discovery of Traditional Chinese Medicine Prescription Patterns Containing Herbal Dosage Based on Multilevel Top-K Weighted Association RulesComputational Intelligence and Neuroscience10.1155/2022/54660112022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5466011
Shawkat MBadawi MEl-ghamrawy SArnous REl-desoky A(2022)An optimized FP-growth algorithm for discovery of association rulesThe Journal of Supercomputing10.1007/s11227-021-04066-y78:4(5479-5506)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11227-021-04066-y
Tung NNguyen LNguyen TVo B(2022)An efficient method for mining multi-level high utility ItemsetsApplied Intelligence10.1007/s10489-021-02681-z52:5(5475-5496)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02681-z
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Index Terms

Mining Multiple-Level Association Rules in Large Databases
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning settings
2. Information systems
  1. Data management systems
    1. Database management system engines
      1. Triggers and rules
  2. Information systems applications
    1. Data mining

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Published In

cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 11, Issue 5

September 1999

120 pages

ISSN:1041-4347

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Copyright © Copyright © 1999 IEEE. All Rights Reserved.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 September 1999

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Cited By

Zhu X(2022)Discovery of Traditional Chinese Medicine Prescription Patterns Containing Herbal Dosage Based on Multilevel Top-K Weighted Association RulesComputational Intelligence and Neuroscience10.1155/2022/54660112022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5466011
Shawkat MBadawi MEl-ghamrawy SArnous REl-desoky A(2022)An optimized FP-growth algorithm for discovery of association rulesThe Journal of Supercomputing10.1007/s11227-021-04066-y78:4(5479-5506)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s11227-021-04066-y
Tung NNguyen LNguyen TVo B(2022)An efficient method for mining multi-level high utility ItemsetsApplied Intelligence10.1007/s10489-021-02681-z52:5(5475-5496)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02681-z
Bayani DMitsch S(2022)Fanoos: Multi-resolution, Multi-strength, Interactive Explanations for Learned SystemsVerification, Model Checking, and Abstract Interpretation10.1007/978-3-030-94583-1_3(43-68)Online publication date: 16-Jan-2022
https://dl.acm.org/doi/10.1007/978-3-030-94583-1_3
Gaur PReddy PSwamy MMondal A(2020)A Revenue-Based Product Placement Framework to Improve Diversity in Retail BusinessesBig Data Analytics10.1007/978-3-030-66665-1_19(289-307)Online publication date: 15-Dec-2020
https://dl.acm.org/doi/10.1007/978-3-030-66665-1_19
Fournier-Viger PWang YLin JLuna JVentura S(2020)Mining Cross-Level High Utility ItemsetsTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices10.1007/978-3-030-55789-8_73(858-871)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-55789-8_73
Aman RAhmed C(2018)Mining Cross-Level Closed Sequential PatternsAdvances in Data Mining. Applications and Theoretical Aspects10.1007/978-3-319-95786-9_15(199-214)Online publication date: 11-Jul-2018
https://dl.acm.org/doi/10.1007/978-3-319-95786-9_15
Liu CZhang KXiong HJiang GYang Q(2016)Temporal Skeletonization on Sequential Data: Patterns, Categorization, and VisualizationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.246871528:1(211-223)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2468715
Weng C(2016)Identifying association rules of specific later-marketed productsApplied Soft Computing10.1016/j.asoc.2015.09.04738:C(518-529)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.asoc.2015.09.047
Antonelli DBaralis EBruno GCagliero LCerquitelli TChiusano SGarza PMahoto N(2015)MeTAACM Transactions on Intelligent Systems and Technology10.1145/27004796:4(1-25)Online publication date: 15-Jul-2015
https://dl.acm.org/doi/10.1145/2700479
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