Discrete approach for automatic knowledge extraction from precedent large-scale data, and classification

The proposed method for automatic knowledge extraction from large-scale data is based on the idea of analysing neighborhoods of "supporting" objects and construction of data covered by sets of hyper parallelepipeds. A simple procedure to choose the supporting objects is applied. Knowledge extraction (logical regularities search) is based on the solution of special discrete linear optimization tasks associated with supporting objects. Two practical tasks are considered for method illustration.