Mining fuzzy association rules from questionnaire data |
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| Authors: | Yen-Liang Chen Cheng-Hsiung Weng |
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| Affiliation: | 1. Center for Applied Mathematics of Tianjin University, Tianjin 300072, PR China;2. Department of Mathematics, School of Science, Tianjin University, Tianjin 300072, PR China;3. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, PR China;1. Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China;2. Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA;3. Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China;1. Department of Ophthalmology, Zekai Tahir Burak Women''s Health Training and Research Hospital, Ankara, Turkey;2. Department of Ophthalmology, Faculty of Medicine, Hacettepe University, Ankara, Turkey;1. Mälardalen University, Högskoleplan 1, Västerås 722 20, Sweden;2. Eastern European Lesya Ukrainka National University, prospect Voli, 13, Lutsk 43025, Ukraine |
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| 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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