Article

Mining confident rules without support requirement

Authors:

Ke Wang,

Yu He,

David W. CheungAuthors Info & Claims

CIKM '01: Proceedings of the tenth international conference on Information and knowledge management

Pages 89 - 96

https://doi.org/10.1145/502585.502601

Published: 05 October 2001 Publication History

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Abstract

An open problem is to find all rules that satisfy a minimum confidence but not necessarily a minimum support. Without the support requirement, the classic support-based pruning strategy is inapplicable. The problem demands a confidence-based pruning strategy. In particular, the following monotonicity of confidence, called the universal-existential upward closure, holds: if a rule of size k is confident (for the given minimum confidence), for every other attribute not in the rule, some specialization of size k+1 using the attribute must be confident. Like the support-based pruning, the bottleneck is at the memory that often is too small to store the candidates required for search. We implement this strategy on disk and study its performance.

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Cited By

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Kaur PGoel STyagi AMalik SShrivastava U(2024)SAFARM: simulated annealing based framework for association rule miningInternational Journal of Information Technology10.1007/s41870-024-02079-317:3(1523-1532)Online publication date: 5-Aug-2024
https://doi.org/10.1007/s41870-024-02079-3
FAKIR YEl AYACHI RFAKIR M(2020)Mining Frequent Pattern by Titanic and FP-Tree algorithmsInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT206537(208-215)Online publication date: 2-Oct-2020
https://doi.org/10.32628/CSEIT206537
Lu SChiang DKeh HHuang H(2019)Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold valuesKnowledge-Based Systems10.1016/j.knosys.2010.04.00423:6(598-604)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.knosys.2010.04.004
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Index Terms

Mining confident rules without support requirement
1. Information systems
  1. Data management systems
    1. Database management system engines
      1. Triggers and rules
  2. Information systems applications
    1. Data mining
2. Theory of computation
  1. Randomness, geometry and discrete structures

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Published In

CIKM '01: Proceedings of the tenth international conference on Information and knowledge management

October 2001

616 pages

ISBN:1581134363

DOI:10.1145/502585

Editors:
Henrique Paques
Georgia Institute of Technology
,
Ling Liu
Georgia Institute of Technology
,
David Grossman
Illinois Institute of Technology
,
General Chair:
Calton Pu
Georgia Institute of Technology

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

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Published: 05 October 2001

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October 5 - 10, 2001

Georgia, Atlanta, USA

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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View all

Kaur PGoel STyagi AMalik SShrivastava U(2024)SAFARM: simulated annealing based framework for association rule miningInternational Journal of Information Technology10.1007/s41870-024-02079-317:3(1523-1532)Online publication date: 5-Aug-2024
https://doi.org/10.1007/s41870-024-02079-3
FAKIR YEl AYACHI RFAKIR M(2020)Mining Frequent Pattern by Titanic and FP-Tree algorithmsInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology10.32628/CSEIT206537(208-215)Online publication date: 2-Oct-2020
https://doi.org/10.32628/CSEIT206537
Lu SChiang DKeh HHuang H(2019)Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold valuesKnowledge-Based Systems10.1016/j.knosys.2010.04.00423:6(598-604)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.knosys.2010.04.004
Zhang SJin ZLu J(2019)Summary queries for frequent itemsets miningJournal of Systems and Software10.1016/j.jss.2009.09.02683:3(405-411)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1016/j.jss.2009.09.026
Zhang SLu JZhang C(2019)A fuzzy logic based method to acquire user threshold of minimum-support for mining association rulesInformation Sciences: an International Journal10.1016/j.ins.2003.09.017164:1-4(1-16)Online publication date: 5-Jan-2019
https://dl.acm.org/doi/10.1016/j.ins.2003.09.017
Zhang SWu XZhang CLu J(2018)Computing the minimum-support for mining frequent patternsKnowledge and Information Systems10.5555/3225662.322597415:2(233-257)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.5555/3225662.3225974
(2018)Rare association rule miningInternational Journal of Knowledge Engineering and Data Mining10.5555/3212241.32122434:3-4(204-258)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.5555/3212241.3212243
(2018)Rare association rule miningInternational Journal of Knowledge Engineering and Data Mining10.5555/3212228.32122304:3-4(204-258)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.5555/3212228.3212230
Bui DHadzic FTagarelli AHecker M(2018)Evaluation of an associative classifier based on position-constrained frequent/closed subtree miningJournal of Intelligent Information Systems10.1007/s10844-014-0312-945:3(397-421)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-014-0312-9
Xiong HTan PKumar V(2018)Hyperclique pattern discoveryData Mining and Knowledge Discovery10.1007/s10618-006-0043-913:2(219-242)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1007/s10618-006-0043-9
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On discovery of maximal confident rules without support pruning in microarray data

Mining confident co-location rules without a support threshold

An Efficient Algorithm for Mining Association Rules Using Confident Frequent Itemsets

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