Type 2 fuzzy set analysis in management surveys
Numerical data from human sources is often used in management studies. MBAs' attitudes and perceptions about the commitment to their school were collected. In an earlier paper, the utility allowing respondents to draw fuzzy membership functions over the set of questionnaire answers was explored...
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2014
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th-cmuir.6653943832-13072014-08-29T09:29:07Z Type 2 fuzzy set analysis in management surveys Auephanwiriyakul S. Adrian A. Keller J.M. Numerical data from human sources is often used in management studies. MBAs' attitudes and perceptions about the commitment to their school were collected. In an earlier paper, the utility allowing respondents to draw fuzzy membership functions over the set of questionnaire answers was explored. This response format produced good qualitative information. In this paper, we look at a quantitative analysis of these linguistic responses. In particular, we develop a linguistic nearest prototype and computational efficient linguistic Hard C-Means for vectors of fuzzy sets and apply these algorithms to such "linguistic vectors" derived from a set of forty-nine subjects answering questions about students' commitment to their university. 2014-08-29T09:29:07Z 2014-08-29T09:29:07Z 2002 Conference Paper 10987584 60408 PIFSF http://www.scopus.com/inward/record.url?eid=2-s2.0-0036456255&partnerID=40&md5=0bd3ffb07bd83c2fb534fae1565a2f34 http://cmuir.cmu.ac.th/handle/6653943832/1307 English |
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Numerical data from human sources is often used in management studies. MBAs' attitudes and perceptions about the commitment to their school were collected. In an earlier paper, the utility allowing respondents to draw fuzzy membership functions over the set of questionnaire answers was explored. This response format produced good qualitative information. In this paper, we look at a quantitative analysis of these linguistic responses. In particular, we develop a linguistic nearest prototype and computational efficient linguistic Hard C-Means for vectors of fuzzy sets and apply these algorithms to such "linguistic vectors" derived from a set of forty-nine subjects answering questions about students' commitment to their university. |
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Conference or Workshop Item |
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Auephanwiriyakul S. Adrian A. Keller J.M. |
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Auephanwiriyakul S. Adrian A. Keller J.M. Type 2 fuzzy set analysis in management surveys |
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Auephanwiriyakul S. Adrian A. Keller J.M. |
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Auephanwiriyakul S. |
title |
Type 2 fuzzy set analysis in management surveys |
title_short |
Type 2 fuzzy set analysis in management surveys |
title_full |
Type 2 fuzzy set analysis in management surveys |
title_fullStr |
Type 2 fuzzy set analysis in management surveys |
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Type 2 fuzzy set analysis in management surveys |
title_sort |
type 2 fuzzy set analysis in management surveys |
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2014 |
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http://www.scopus.com/inward/record.url?eid=2-s2.0-0036456255&partnerID=40&md5=0bd3ffb07bd83c2fb534fae1565a2f34 http://cmuir.cmu.ac.th/handle/6653943832/1307 |
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