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A Structural Analysis Based on Similarity between Fuzzy Clusters and Its Application to Evaluation Data

  • Ryunosuke Chiba
  • Toshiaki Furutani
  • Mika Sato-Ilic
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)

Abstract

This paper presents a similarity of fuzzy clusters based analysis for consumer preference evaluation data. We apply this method to multiple types of evaluation data. The merit of this method is to capture the latent structure of consumer preferences represented by fuzzy clusters and the relation between these fuzzy clusters as similarity. In addition, due to the presence of identical fuzzy clusters over the different industries, we can compare the difference between the industries through the same subject evaluation by using the scale of the fuzzy clusters. We show a better performance from the use of our proposed method with several numerical examples.

Keywords

Fuzzy Cluster Partition Matrix Fuzzy Cluster Method Cluster Difference Compare Response Rate 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ryunosuke Chiba
    • 1
  • Toshiaki Furutani
    • 1
  • Mika Sato-Ilic
    • 2
  1. 1.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan

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