• DocumentCode
    952813
  • Title

    Switching regression models and fuzzy clustering

  • Author

    Hathaway, Richard J. ; Bezdek, James C.

  • Author_Institution
    Dept. of Math. & Comput. Sci., Georgia Southern Univ., Statesboro, GA, USA
  • Volume
    1
  • Issue
    3
  • fYear
    1993
  • fDate
    8/1/1993 12:00:00 AM
  • Firstpage
    195
  • Lastpage
    204
  • Abstract
    A family of objective functions called fuzzy c-regression models, which can be used too fit switching regression models to certain types of mixed data, is presented. Minimization of particular objective functions in the family yields simultaneous estimates for the parameters of c regression models, together with a fuzzy c-partitioning of the data. A general optimization approach for the family of objective functions is given and corresponding theoretical convergence results are discussed. The approach is illustrated by two numerical examples that show how it can be used to fit mixed data to coupled linear and nonlinear models
  • Keywords
    convergence of numerical methods; fuzzy set theory; minimisation; parameter estimation; statistical analysis; convergence; fuzzy c-regression models; fuzzy clustering; minimisation; mixed data; objective functions; parameter estimation; switching regression models; Clustering algorithms; Computer science; Convergence; Couplings; Covariance matrix; Fuzzy sets; Linear approximation; Marine animals; Parameter estimation; Yield estimation;
  • fLanguage
    English
  • Journal_Title
    Fuzzy Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1063-6706
  • Type

    jour

  • DOI
    10.1109/91.236552
  • Filename
    236552