| The purpose of this paper was to introduce the fundamental concepts, computational methods, two practical examples, and the implementation of calculations using SAS software related to the k-nearest neighbor (k-NN) discriminant analysis method 4. The fundamental concepts included distance metrics, selection of k-value, feature standardization, classification and regression, and lazy learning. The computational methods covered basic definitions and relevant formulas. The two datasets used in the examples were "measurement results of five quantitative indicators from samples in three stratigraphic sections" and "data related to breakfast cereals produced by three major U.S. manufacturers." Using SAS software, k-nearest neighbor discriminant analysis was performed on the data from these two examples, and the SAS output results were interpreted. |