Real datasets are often distributed nonlinearly. Although many least squares support vector machine (LS-SVM) methods have successfully modeled this kind of data using a divide-and-conquer strategy, they are often ineffective when nonlinear data are subject to noise due to a lack of robustness within each sub-model. In this paper, a robust clustered LS-SVM is proposed to model this type of data. First, the clustering method is used to divide the sample data into several sub-datasets. A local robust LS-SVM model is then developed to capture the l...
