Least squares model fitting to fuzzy vector data
Fuzzy Sets and Systems
Mixtures of linear regressions
Computational Statistics & Data Analysis
Applying fuzzy linear regression to VDT legibility
Fuzzy Sets and Systems
Fuzzy sets in decision analysis, operations research and statistics
A least-squares approach to fuzzy linear regression analysis
Computational Statistics & Data Analysis
Fuzzy regression methods—a comparative assessment
Fuzzy Sets and Systems
Multidimensional least-squares fitting with a fuzzy model
Fuzzy Sets and Systems
Outliers detection and confidence interval modification in fuzzy regression
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An "orderwise" polynomial regression procedure for fuzzy data
Fuzzy Sets and Systems
Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
Computational Statistics & Data Analysis
Clusters, outliers, and regression: fixed point clusters
Journal of Multivariate Analysis
Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques
Journal of Classification
Fuzzy nonparametric regression based on local linear smoothing technique
Information Sciences: an International Journal
Asymptotic properties of least squares estimation with fuzzy observations
Information Sciences: an International Journal
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Information Sciences: an International Journal
Estimation of a simple linear regression model for fuzzy random variables
Fuzzy Sets and Systems
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Engineering Applications of Artificial Intelligence
Pattern Recognition
Linear grouping using orthogonal regression
Computational Statistics & Data Analysis
Least squares estimation of a linear regression model with LR fuzzy response
Computational Statistics & Data Analysis
Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable
Computational Statistics & Data Analysis
Information Sciences: an International Journal
Robust clusterwise linear regression through trimming
Computational Statistics & Data Analysis
Information Sciences: an International Journal
On cluster-wise fuzzy regression analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Alpha-Cut Implemented Fuzzy Clustering Algorithms and Switching Regressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Robust fuzzy regression analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Functional fuzzy clusterwise regression analysis
Advances in Data Analysis and Classification
Hi-index | 0.07 |
In this paper we introduce a class of fuzzy clusterwise regression models with LR fuzzy response variable and numeric explanatory variables, which embodies fuzzy clustering, into a fuzzy regression framework. The model bypasses the heterogeneity problem that could arise in fuzzy regression by subdividing the dataset into homogeneous clusters and performing separate fuzzy regression on each cluster. The integration of the clustering model into the regression framework allows us to simultaneously estimate the regression parameters and the membership degree of each observation to each cluster by optimizing a single objective function. The class of models proposed here includes, as special cases, the fuzzy clusterwise linear regression model and the fuzzy clusterwise polynomial regression model. We also introduce a set of goodness of fit indices to evaluate the fit of the regression model within each cluster as well as in the whole dataset. Finally, we consider some cluster validity criteria that are useful in identifying the ''optimal'' number of clusters. Several applications are provided in order to illustrate the approach.