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A Study on Clustering Kansei Factors for the Surface Roughenss of Materials

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dc.contributor.author전창림-
dc.contributor.authorKyungmeeChoi-
dc.date.accessioned2022-03-14T09:42:16Z-
dc.date.available2022-03-14T09:42:16Z-
dc.date.created2022-03-14-
dc.date.issued2003-
dc.identifier.issn2287-7843-
dc.identifier.urihttps://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/26535-
dc.description.abstractThe human sensibility product design requires information on consumer's emotions such as vision, auditory, olfactory, gustatory, or tactile perceptions. In this study, tactile sense which has not been well studied compared to other senses, is measured and statistically analysed. The emotional responses of 37 pairs of positive and negative adjectives describing tactile senses are collected and analysed through the questionnaire to find the correlation between adjectives and surface roughness of the sample. Mean ranks for 37 pairs of adjectives on four samples are obtained, and used to cluster these adjectives by factor analysis, multidimensional scaling, or cluster analysis.-
dc.publisher한국통계학회-
dc.titleA Study on Clustering Kansei Factors for the Surface Roughenss of Materials-
dc.title.alternativeA Study on Clustering Kansei Factors for the Surface Roughenss of Materials-
dc.typeArticle-
dc.contributor.affiliatedAuthor전창림-
dc.identifier.bibliographicCitationCommunications for Statistical Applications and Methods, v.10, no.1, pp.49 - 60-
dc.relation.isPartOfCommunications for Statistical Applications and Methods-
dc.citation.titleCommunications for Statistical Applications and Methods-
dc.citation.volume10-
dc.citation.number1-
dc.citation.startPage49-
dc.citation.endPage60-
dc.type.rimsART-
dc.identifier.kciidART000900085-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorKansei Engineering-
dc.subject.keywordAuthorFactor Analysis-
dc.subject.keywordAuthorMultidimensional Scaling-
dc.subject.keywordAuthorCluster analysis-
dc.subject.keywordAuthorKansei Engineering-
dc.subject.keywordAuthorFactor Analysis-
dc.subject.keywordAuthorMultidimensional Scaling-
dc.subject.keywordAuthorCluster analysis-
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