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Mining fuzzy association rules from questionnaire data

✍ Scribed by Yen-Liang Chen; Cheng-Hsiung Weng

Publisher
Elsevier Science
Year
2009
Tongue
English
Weight
725 KB
Volume
22
Category
Article
ISSN
0950-7051

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✦ Synopsis

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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