An Optimized Discrete Data Classification Method in N-Dimensional

We propose a discrete data classification method of scattered data in N-dimensional by solving the minimax problem for a set of points. The current research is extended from 2-dimensional and 3-dimensional to N-dimensional. The problem can be applied to artificial intelligence classification problems (machine learning, deep learning), point data analysis problems (data science problem), the optimized design of nanoscale circuits, and the location of facility problems, circle detection on 2D image, or sphere detection on depth image. We generalized the discrete data classification methodology in N-dimensional. Finally, we resolved to find an exact solution of the location of a manifold for our suggested problem in N-dimensional.