Fuzzy least-squares linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems - Information processing
A new approach to fuzzy regression models with application to business cycle analysis
Fuzzy Sets and Systems
Fuzzy least-squares algorithms for interactive fuzzy linear regression models
Fuzzy Sets and Systems - Theme: Modeling and learning
The Journal of Machine Learning Research
Estimation of weibull parameters using a fuzzy least-squares method
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Information Sciences: an International Journal
A fuzzy clustering methodology for linguistic opinions in group decision making
Applied Soft Computing
Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable
Computational Statistics & Data Analysis
Solving Fuzzy Linear Regression with Hybrid Optimization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Fuzzy clustering with viewpoints
IEEE Transactions on Fuzzy Systems
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
A theoretical development on a fuzzy distance measure for fuzzy numbers
Mathematical and Computer Modelling: An International Journal
Fuzzy distance of triangular fuzzy numbers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Since Tanaka et al. (1982) proposed a study of linear regression analysis with a fuzzy model, fuzzy regression analysis has been widely studied and applied in a variety of substantive areas. Regression analysis in the case of heterogeneity of observations is commonly presented in practice. The authors' main goal is to apply fuzzy clustering techniques to fuzzy regression analysis. Fuzzy clustering is used to overcome the heterogeneous problem in the fuzzy regression model. They present the cluster-wise fuzzy regression analysis in two approaches: the two-stage weighted fuzzy regression and the one-stage generalized fuzzy regression. The two-stage procedure extends the results of Jajuga (1986) and Diamond (1988). The one-stage approach is created by embedding fuzzy clusterings into the fuzzy regression model fitting at each step of procedure. This kind of embedding in the one-stage procedure is more effective since the structure of regression line shape encountered in the data set is taken into account at each iteration of the algorithm. Numerical results give evidence that the one-stage procedure can be highly recommended in cluster-wise fuzzy regression analysis