• DocumentCode
    1694210
  • Title

    Fuzzy claustering application to marketing data and feature extraction of data

  • Author

    Tomoko, Ninomiya

  • Author_Institution
    Tamagawa Univ.
  • fYear
    2008
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Usually, the population of a large marketing dataset is assumed that a mixture of two or more different groups. In this paper, to classify the observations into the group of some different populations, we propose a fuzzy clustering technique that uses an unobserved variable that influences the observed variables. And, we show an effectiveness of the technique to clarify the data structure and to extract the feature.
  • Keywords
    data mining; data structures; feature extraction; fuzzy set theory; marketing data processing; pattern clustering; data feature extraction; data structure; fuzzy clustering; marketing data; marketing dataset; Computer performance; Data analysis; Data mining; Data structures; Educational institutions; Feature extraction; Fuzzy sets; Information technology; Internet; Proposals; Fuzzy clustering; Marketing data; observed Variable; unobserved variable;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Automation Congress, 2008. WAC 2008. World
  • Conference_Location
    Hawaii, HI
  • Print_ISBN
    978-1-889335-38-4
  • Electronic_ISBN
    978-1-889335-37-7
  • Type

    conf

  • Filename
    4698948