A Proposal for Comparison of Impression Evaluation Data Among Individuals by Using Clustering Method Based on Distributed Structure of Data

  • Shou Kuroda
  • Tomohiro Yoshikawa
  • Takeshi Furuhashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)


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 (OPA). 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.


Cluster Center Dissimilarity Measure Multivariate Statistical Model Semantic Differential Distribution Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Takashi, M., Pieter, M.K.: Three-mode models and individual differences in semantic differential data. Multivariate Behavioral Research 38(2), 247–283 (2003)CrossRefGoogle Scholar
  2. 2.
    Nakamori, Y., Kawanaka, A.: Expression of vagueness in factor space. Japan Society for Fuzzy Theory and Intelligent Informatics (in Japanese) 11(5), 797–807 (1999)Google Scholar
  3. 3.
    Toyoda, H.: An exploratory positioning analysis: Three-mode multivariate analysis for semantic differential data. The Japanese Journal of Psychology 72(3), 213–218 (2001)MathSciNetGoogle Scholar
  4. 4.
    Yamamoto, K., Kojima, T., Yoshikawa, T., Furuhashi, T.: A basic study on discovering relationships of impression words among individuals using visualization method. In: IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO) (2005)Google Scholar
  5. 5.
    Akca, M.D.: Generalized procrustes analysis and its applications in photogrammetry. Institute of Geodesy and Photogrammetry, Swiss Federal Institute of Technology Zurich (ETHZ) (2003)Google Scholar
  6. 6.
    Yamamoto, K., Yoshikawa, T., Furuhashi, T.: A proposal on stratification method for SD evaluation data considering individuality. In: FAN Symposium 2005 in Kyoto, Society of Instrument and Control Enginners (in Japanese), pp. 495–500 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shou Kuroda
    • 1
  • Tomohiro Yoshikawa
    • 1
  • Takeshi Furuhashi
    • 1
  1. 1.Dept. of Computational Science and EngineeringNagoya UniversityNagoyaJapan

Personalised recommendations