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  1. B200 工学部/工学研究科
  2. B200e 会議資料
  3. 国際会議

Application of Clustering Method based on Orthogonal Procrustes Analysis to Analysis of Questionnaire Data

http://hdl.handle.net/2237/20675
http://hdl.handle.net/2237/20675
4cdf8b4a-c9e4-4e99-90c7-77b69e1478fe
名前 / ファイル ライセンス アクション
2008_410.pdf 2008_410.pdf (582.9 kB)
Item type 会議発表論文 / Conference Paper(1)
公開日 2014-11-04
タイトル
タイトル Application of Clustering Method based on Orthogonal Procrustes Analysis to Analysis of Questionnaire Data
言語 en
著者 Yoshikawa, Tomohiro

× Yoshikawa, Tomohiro

WEKO 53952

en Yoshikawa, Tomohiro

Search repository
Yamaga, Shinichiro

× Yamaga, Shinichiro

WEKO 53953

en Yamaga, Shinichiro

Search repository
Furuhashi, Takeshi

× Furuhashi, Takeshi

WEKO 53954

en Furuhashi, Takeshi

Search repository
アクセス権
アクセス権 open access
アクセス権URI http://purl.org/coar/access_right/c_abf2
キーワード
主題Scheme Other
主題 Questionnaire Data
キーワード
主題Scheme Other
主題 Clustering
キーワード
主題Scheme Other
主題 Orthogonal Procrustes Analysis
抄録
内容記述 In the field of marketing, companies often carry out a questionnaire to consumers for grasping their impressions of products. Analyzing the evaluation data obtained from consumers enables us to grasp the tendency of the market and to find problems and/or to make hypotheses that are useful for the development of products. Semantic Differential (SD) method is one of the most useful methods for quantifying human-impressions to the objects. The purpose of this study is to develop a method for visualization of individual features in data. This paper proposes the Clustering method based on Orthogonal Procrustes Analysis(COPA). The proposed method can cluster subjects among whom the distributed structures of the SD evaluation data are similar. The analysis by this method leads to discovery of majority/minority groups and/or groups which have unique features.In addition, it enables us to analyze the similarity/difference of objects and impression words among clusters and/or subjects by comparing the cluster centers and/or transformation matrices. This paper applies the proposed method to an actual SD evaluation data. It shows that this method can investigate the similar relationships among the objects in each group and compare the similarity/difference of impression words used for the evaluation of objects among subjects in the same cluster.
言語 en
内容記述タイプ Abstract
内容記述
内容記述 Joint 4th International Conference on Soft Computing and Intelligent Systems and 9th International Symposium on advanced Intelligent Systems, September 17-21, 2008, Nagoya University, Nagoya, Japan
言語 en
内容記述タイプ Other
内容記述
内容記述 Session ID: TH-A4-3
言語 en
内容記述タイプ Other
出版者
言語 ja
出版者 日本知能情報ファジィ学会
言語
言語 eng
資源タイプ
資源 http://purl.org/coar/resource_type/c_5794
タイプ conference paper
出版タイプ
出版タイプ VoR
出版タイプResource http://purl.org/coar/version/c_970fb48d4fbd8a85
DOI
関連タイプ isVersionOf
識別子タイプ DOI
関連識別子 https://doi.org/10.14864/softscis.2008.0.410.0
書誌情報 en : SCIS & ISIS

巻 2008, p. 410-414, 発行日 2008
著者版フラグ
値 publisher
URI
識別子 http://dx.doi.org/10.14864/softscis.2008.0.410.0
識別子タイプ DOI
URI
識別子 http://hdl.handle.net/2237/20675
識別子タイプ HDL
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