Clustering regression based on interval-valued fuzzy outputs and interval-valued fuzzy parameters

In this paper, a new approach is presented to the problem of the clustering regression models with imprecise quantities. In this approach, the response variable and the parameters of model are assumed to be the interval-valued fuzzy numbers. We introduce two indices to investigate the goodness-of-fit of such models based on the similarity measure and the squared errors. In addition to, the predictive ability of the proposed clustering models is evaluated by using the cross-validation method. Finally, the application of the proposed approach in modeling some soil characteristics is studied.