Using generalized precedents for big data sample compression at learning

The role of intrinsic and introduced data structures at constructing efficient recognition algorithms is analyzed. The concept of generalized precedent as representation of stable local regularity in data and based on its use methods of reduction of the dimension of tasks has been investigated. Two new approaches to the problem based on positional data representation and on cluster means for elementary logical regularities are proposed. The results of computational experiment with data compression in parametric spaces for several practical tasks are presented.