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A Comparison Of K-Means And Fuzzy C-Means Using Background Knowledge

Author

Listed:
  • Goddard, J.
  • de los Cobos Silva, S.G.
  • Gutiérrez Andrade, M.A.

    (Universidad Autónoma Metropolitana)

Abstract

Relevant Component Analysis has been introduced recently as a way to incorporate a priori information, such as class or preference information, that may exist for a given data set. The method uses this information to define a new Mahalanobis distance metric on the data space. The purpose of the present paper is to investigate the difference, if any, that Relevant Component Analysis makes when applied in conjunction with the clustering algorithms of k-means or fuzzy c-means. Results are given for six standard data sets.

Suggested Citation

  • Goddard, J. & de los Cobos Silva, S.G. & Gutiérrez Andrade, M.A., 2006. "A Comparison Of K-Means And Fuzzy C-Means Using Background Knowledge," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(2), pages 3-16, November.
    • Handle: RePEc:fzy:fuzeco:v:xi:y:2006:i:2:p:3-16
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    More about this item

    Keywords

    Consumer behaviour; marketing modelling; model estimation; structural equation modelling; fuzzy association rules; knowledge discovery;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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