| This paper aimed to introduce the fundamental concepts, computational methods, two case studies, and the implementation of k-nearest neighbor (k-NN) discriminant analysis method 3 using SAS. The basic concepts included k-nearest neighbors, distance metrics, discriminant analysis, voting mechanisms, and feature space. The computational methods involved distance measurement formulas and majority voting rules. The datasets for the two case studies were "measurement results of two quantitative indicators from 24 families" and "measurement results of eight skeletal indicators from wild and cultured turtles." With the help of SAS software, k-nearest neighbor discriminant analysis was performed on the data from both case studies, and the output results were interpreted. |