Article

Enhanced mining of association rules from data cubes

Authors:

Riadh Ben Messaoud,

Sabine Loudcher Rabaséda,

Rokia MissaouiAuthors Info & Claims

DOLAP '06: Proceedings of the 9th ACM international workshop on Data warehousing and OLAP

Pages 11 - 18

https://doi.org/10.1145/1183512.1183517

Published: 10 November 2006 Publication History

Abstract

On-line analytical processing (OLAP) provides tools to explore and navigate into data cubes in order to extract interesting information. Nevertheless, OLAP is not capable of explaining relationships that could exist in a data cube. Association rules are one kind of data mining techniques which finds associations among data. In this paper, we propose a framework for mining inter-dimensional association rules from data cubes according to a sum-based aggregate measure more general than simple frequencies provided by the traditional COUNT measure. Our mining process is guided by a meta-rule context driven by analysis objectives and exploits aggregate measures to revisit the definition of support and confidence. We also evaluate the interestingness of mined association rules according to Lift and Loevinger criteria and propose an efficient algorithm for mining inter-dimensional association rules directly from a multidimensional data.

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Cited By

Parashar NTiwari AGupta R(2024)Knowledge discovery in weather forecasting: mining fuzzy image association rules with fine-tuned CNN and fuzzy HIFP algorithmEvolving Systems10.1007/s12530-024-09596-3Online publication date: 25-Jun-2024
https://doi.org/10.1007/s12530-024-09596-3
Bazin AGros NBertaux ANicolle C(2023)Condensed Representations of Association Rules in n-Ary RelationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315370935:5(4598-4607)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3153709
Benouaret IAmer-Yahia SRoy SKamdem-Kengne CChagraoui J(2020)Enabling Decision Support Through Ranking and Summarization of Association Rules for TOTAL CustomersTransactions on Large-Scale Data- and Knowledge-Centered Systems XLIV10.1007/978-3-662-62271-1_6(160-193)Online publication date: 10-Sep-2020
https://doi.org/10.1007/978-3-662-62271-1_6
Show More Cited By

Index Terms

Enhanced mining of association rules from data cubes
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Decision support systems

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cover image ACM Conferences

DOLAP '06: Proceedings of the 9th ACM international workshop on Data warehousing and OLAP

November 2006

110 pages

ISBN:1595935304

DOI:10.1145/1183512

General Chair:
Il-Yeol Song
Drexel University
,
Program Chair:
Panos Vassiliadis
University of Ioannina, Greece

Copyright © 2006 ACM.

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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Published: 10 November 2006

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CIKM06

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CIKM06: Conference on Information and Knowledge Management

November 10, 2006

Virginia, Arlington, USA

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Overall Acceptance Rate 29 of 79 submissions, 37%

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Cited By

Parashar NTiwari AGupta R(2024)Knowledge discovery in weather forecasting: mining fuzzy image association rules with fine-tuned CNN and fuzzy HIFP algorithmEvolving Systems10.1007/s12530-024-09596-3Online publication date: 25-Jun-2024
https://doi.org/10.1007/s12530-024-09596-3
Bazin AGros NBertaux ANicolle C(2023)Condensed Representations of Association Rules in n-Ary RelationsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.315370935:5(4598-4607)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3153709
Benouaret IAmer-Yahia SRoy SKamdem-Kengne CChagraoui J(2020)Enabling Decision Support Through Ranking and Summarization of Association Rules for TOTAL CustomersTransactions on Large-Scale Data- and Knowledge-Centered Systems XLIV10.1007/978-3-662-62271-1_6(160-193)Online publication date: 10-Sep-2020
https://doi.org/10.1007/978-3-662-62271-1_6
Letrache KBeggar ORamdani M(2019)OLAP cube partitioning based on association rules methodApplied Intelligence10.1007/s10489-018-1275-249:2(420-434)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s10489-018-1275-2
Usman MUsman M(2018)Literature ReviewPredictive Analysis on Large Data for Actionable Knowledge10.4018/978-1-5225-5029-7.ch002(14-58)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5029-7.ch002
Usman MUsman M(2018)IntroductionPredictive Analysis on Large Data for Actionable Knowledge10.4018/978-1-5225-5029-7.ch001(1-13)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5029-7.ch001
Fisun MHorban HDvoretskyi M(2018)Methods of Searching for Association Dependencies in Multidimensional Databases2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT)10.1109/STC-CSIT.2018.8526737(88-93)Online publication date: Sep-2018
https://doi.org/10.1109/STC-CSIT.2018.8526737
Rolfsnes TMoonen LAlesio SBehjati RBinkley D(2018)Aggregating Association Rules to Improve Change RecommendationEmpirical Software Engineering10.1007/s10664-017-9560-y23:2(987-1035)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1007/s10664-017-9560-y
Rolfsnes TMoonen LDi Alesio SBehjati RBinkley DKim MRobbes RBird C(2016)Improving change recommendation using aggregated association rulesProceedings of the 13th International Conference on Mining Software Repositories10.1145/2901739.2901756(73-84)Online publication date: 14-May-2016
https://dl.acm.org/doi/10.1145/2901739.2901756
Masmoudi MGhozzi F(2016)Analyses of social WeBhouse integrating SNA metrics2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)10.1109/AICCSA.2016.7945814(1-7)Online publication date: Nov-2016
https://doi.org/10.1109/AICCSA.2016.7945814
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