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Mining association rules between sets of items in large databases

Authors:

Rakesh Agrawal,

Tomasz Imieliński,

Arun SwamiAuthors Info & Claims

SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data

Pages 207 - 216

https://doi.org/10.1145/170035.170072

Published: 01 June 1993 Publication History

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Abstract

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.

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S SChaudhari S(2024)Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N RecommendationsJournal of information and communication convergence engineering10.56977/jicce.2024.22.1.4422:1(44-55)Online publication date: 31-Mar-2024
https://doi.org/10.56977/jicce.2024.22.1.44
Dabla PUpreti KShrivastav DMehta VSingh D(2024)Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitusWorld Journal of Methodology10.5662/wjm.v14.i2.9260814:2Online publication date: 20-Jun-2024
https://doi.org/10.5662/wjm.v14.i2.92608
Viguri Axpe M(2024)COMPLEJIDAD, INTELIGENCIA ARTIFICIAL Y ÉTICARevista Iberoamericana de Complejidad y Ciencias Económicas10.48168/ricce.v2n2p632:2(63-77)Online publication date: 30-Jun-2024
https://doi.org/10.48168/ricce.v2n2p63
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Index Terms

Mining association rules between sets of items in large databases
1. Information systems
  1. Data management systems
    1. Database design and models
    2. Database management system engines
      1. Database transaction processing
  2. Information systems applications

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Information

Published In

SIGMOD '93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data

June 1993

566 pages

ISBN:0897915925

DOI:10.1145/170035

Editors:
Peter Buneman
Univ. of Pennsylvania
,
Sushil Jajodia

ACM SIGMOD Record Volume 22, Issue 2
June 1, 1993
558 pages
ISSN:0163-5808
DOI:10.1145/170036
Editors:
Peter Buneman
Univ. of Pennsylvania
,
Sushil Jajodia,
Won Kim
UniSQL, Inc., Austin, TX
Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 1993

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Conference

SIGMOD/PODS93

Sponsor:

SIGMOD/PODS93: Joint ACM SIGMOD International Conference on Management of Data and ACM SIGMOD

May 25 - 28, 1993

D.C., Washington, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all

S SChaudhari S(2024)Deep Learning Framework with Convolutional Sequential Semantic Embedding for Mining High-Utility Itemsets and Top-N RecommendationsJournal of information and communication convergence engineering10.56977/jicce.2024.22.1.4422:1(44-55)Online publication date: 31-Mar-2024
https://doi.org/10.56977/jicce.2024.22.1.44
Dabla PUpreti KShrivastav DMehta VSingh D(2024)Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitusWorld Journal of Methodology10.5662/wjm.v14.i2.9260814:2Online publication date: 20-Jun-2024
https://doi.org/10.5662/wjm.v14.i2.92608
Viguri Axpe M(2024)COMPLEJIDAD, INTELIGENCIA ARTIFICIAL Y ÉTICARevista Iberoamericana de Complejidad y Ciencias Económicas10.48168/ricce.v2n2p632:2(63-77)Online publication date: 30-Jun-2024
https://doi.org/10.48168/ricce.v2n2p63
Demircioğlu Diren DBoran S(2024)Classification of Quality Defects using Multivariate Control Chart with Ensemble Machine Learning ModelJournal of Intelligent Systems: Theory and Applications10.38016/jista.15164537:2(129-144)Online publication date: 26-Sep-2024
https://doi.org/10.38016/jista.1516453
Yin P(2024)Mining Associations between Air Quality and Natural and Anthropogenic FactorsSustainability10.3390/su1611461416:11(4614)Online publication date: 29-May-2024
https://doi.org/10.3390/su16114614
Liu YSun CYang XJia ZSu JGuo Z(2024)A Transformer Heavy Overload Spatiotemporal Distribution Prediction Ensemble under Imbalanced and Nonlinear Data ScenariosSustainability10.3390/su1608311016:8(3110)Online publication date: 9-Apr-2024
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Wang XCao STan QXie ZXia YZheng LLiu JZhou KXiao JRen T(2024)Exploration Vectors and Indicators Extracted by Factor Analysis and Association Rule Algorithms at the Lintan Carlin-Type Gold Deposit, Youjiang Basin, ChinaMinerals10.3390/min1405049214:5(492)Online publication date: 7-May-2024
https://doi.org/10.3390/min14050492
Lowin M(2024)A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language ModelsMachine Learning and Knowledge Extraction10.3390/make60100136:1(233-258)Online publication date: 26-Jan-2024
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Marques JCarvalho BGuedes LDa Costa-Abreu M(2024)Using Association Rules to Obtain Sets of Prevalent Symptoms throughout the COVID-19 Pandemic: An Analysis of Similarities between Cases of COVID-19 and Unspecified SARS in São Paulo-BrazilInternational Journal of Environmental Research and Public Health10.3390/ijerph2109116421:9(1164)Online publication date: 1-Sep-2024
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Yosef ARoth IShnaider EBaranes ASchneider M(2024)Horizontal Learning Approach to Discover Association RulesComputers10.3390/computers1303006213:3(62)Online publication date: 28-Feb-2024
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Mining Multiple-Level Association Rules in Large Databases

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