article

Mining fuzzy association rules from questionnaire data

Authors:

Yen-Liang Chen,

Cheng-Hsiung WengAuthors Info & Claims

Knowledge-Based Systems, Volume 22, Issue 1

Pages 46 - 56

https://doi.org/10.1016/j.knosys.2008.06.003

Published: 01 January 2009 Publication History

Abstract

Association rule mining is one of most popular data analysis methods that can discover associations within data. Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms.

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Cited By

Li RCui WSong TXie XDing RWang YZhang HZhou HWu Y(2024)Causality-Based Visual Analysis of Questionnaire ResponsesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332737630:1(638-648)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327376
Hogo M(2022)The Design of Academic Programs Using Rough Set Association Rule MiningApplied Computational Intelligence and Soft Computing10.1155/2022/16999762022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1699976
Kikuchi MShiramatsu SKozakai ROzono T(2021)Matching Social Issues to Technologies for Civic Tech by Association Rule Mining using Weighted Casual ConfidenceIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3498851.3498931(68-75)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3498851.3498931
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Index Terms

Mining fuzzy association rules from questionnaire data
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
      1. Rule learning
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 22, Issue 1

January, 2009

115 pages

ISSN:0950-7051

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Copyright © Elsevier B.V. © 2008.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2009

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Cited By

Li RCui WSong TXie XDing RWang YZhang HZhou HWu Y(2024)Causality-Based Visual Analysis of Questionnaire ResponsesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332737630:1(638-648)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3327376
Hogo M(2022)The Design of Academic Programs Using Rough Set Association Rule MiningApplied Computational Intelligence and Soft Computing10.1155/2022/16999762022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1699976
Kikuchi MShiramatsu SKozakai ROzono T(2021)Matching Social Issues to Technologies for Civic Tech by Association Rule Mining using Weighted Casual ConfidenceIEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology10.1145/3498851.3498931(68-75)Online publication date: 14-Dec-2021
https://dl.acm.org/doi/10.1145/3498851.3498931
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Jegatha Deborah LBaskaran RKannan A(2014)Learning styles assessment and theoretical origin in an E-learning scenarioArtificial Intelligence Review10.1007/s10462-012-9344-042:4(801-819)Online publication date: 1-Dec-2014
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Camastra FCiaramella AGiovannelli VLener MRastelli VStaiano AStaiano GStarace A(2013)Environmental Risk Assessment of Genetically Modified Organisms by a Fuzzy Decision Support SystemProceedings of the ICIAP 2013 International Workshops on New Trends in Image Analysis and Processing ICIAP 2013 - Volume 815810.1007/978-3-642-41190-8_46(428-435)Online publication date: 9-Sep-2013
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Garcia-Constantino MCoenen FNoble PRadford ASetzkorn C(2012)A semi-automated approach to building text summarisation classifiersProceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-31537-4_39(495-509)Online publication date: 13-Jul-2012
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Garcia-Constantino MCoenen FNoble PRadford ASetzkorn CTierney A(2011)An investigation concerning the generation of text summarisation classifiers using secondary dataProceedings of the 7th international conference on Machine learning and data mining in pattern recognition10.5555/2033831.2033865(387-398)Online publication date: 30-Aug-2011
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