PROCESSING OF LARGE-DIMENSIONAL STATISTICAL DATA ARRAYS WITH JUSTIFICATION OF THE CLUSTERING RULE
Section: GENERAL SECTION
Authors:
1.
Voronezh institute of the Russian Federal penitentiary service
Russian Federation
Russian Federation
2.
Voronezh State Technical University
Russian Federation
Russian Federation
3.
Voronezh State Technical University
Russian Federation
Russian Federation
Type:
Сonference article
Pages:
from 50
to 55
Published:
30.12.2025
Subject area:
UDC 549.22
Language:
Russian
Keywords:
data clustering, statistical arrays, least squares method, spatial regression
Abstract (English):
The analysis of the reduction of the dimensionality of the initial statistical data was carried out using the cluster rule with grouping of independent parameters to ensure the arrangement of their average value in groups in ascending order. This made it possible to formulate the problem of finding the coefficients of the linear equation of spatial regression, which was solved by projecting the values of the dependent variable onto coordinate planes using the least squares method and the objective function in the form of superpositions of squared deviations. Projecting one of the regression equations of the 2-D format onto a plane parallel to the axis of the values of the dependent variable made it possible to identify a straight line in space through a parametric regression equation. An example of calculation is given.
The analysis of the reduction of the dimensionality of the initial statistical data was carried out using the cluster rule with grouping of independent parameters to ensure the arrangement of their average value in groups in ascending order. This made it possible to formulate the problem of finding the coefficients of the linear equation of spatial regression, which was solved by projecting the values of the dependent variable onto coordinate planes using the least squares method and the objective function in the form of superpositions of squared deviations. Projecting one of the regression equations of the 2-D format onto a plane parallel to the axis of the values of the dependent variable made it possible to identify a straight line in space through a parametric regression equation. An example of calculation is given.
Keywords:
data clustering, statistical arrays, least squares method, spatial regression
data clustering, statistical arrays, least squares method, spatial regression
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