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
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