Mining fuzzy association rules from questionnaire data

Yen Liang Chen, Cheng Hsiung Weng

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

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.

Original languageEnglish
Pages (from-to)46-56
Number of pages11
JournalKnowledge-Based Systems
Volume22
Issue number1
DOIs
StatePublished - Jan 2009

Keywords

  • Association rules
  • Data mining
  • Fuzzy sets
  • Questionnaire data

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