ON THE ISSUE OF PRACTICAL APPLICATION OF CLUSTER ANALYSIS ALGORITHMS
Authors:
1.
Novomoskovsk institute of Mendeleyev University of Chemical Technology of Russia
(Department of Computing Technology and Information Technologies, Head of the Department)
2.
Novomoskovsk institute of Mendeleyev University of Chemical Technology of Russia
(Senior lecturer)
Type:
Article
Pages:
from 11
to 17
Status:
Published
Received:
10.10.2023
Accepted:
26.01.2024
Published:
26.01.2024
Subject area:
UDK 004 Информационные технологии. Компьютерные технологии. Теория вычислительных машин и систем
Language:
Russian
Keywords:
cluster analysis, cluster, function of distances between vectors, dendrogram, matrix, measure of divergence, method of grouping multiple objects
Abstract (English):
Data in a broad sense refers to the factual material that provides information for the problem being studied and provides the basis for discussion, analysis and decision making. Cluster analysis is a procedure that, based on a given rule, combines objects or variables into groups. The work provides grouping of multidimensional data using such proximity measures as the sample correlation coefficient and its modulus, the cosine of the angle between vectors, and the Euclidean distance. Grouping is carried out by centers, by nearest neighbor and by selected standards. The program is written in the VS environment in C++.
Data in a broad sense refers to the factual material that provides information for the problem being studied and provides the basis for discussion, analysis and decision making. Cluster analysis is a procedure that, based on a given rule, combines objects or variables into groups. The work provides grouping of multidimensional data using such proximity measures as the sample correlation coefficient and its modulus, the cosine of the angle between vectors, and the Euclidean distance. Grouping is carried out by centers, by nearest neighbor and by selected standards. The program is written in the VS environment in C++.
Keywords:
cluster analysis, cluster, function of distances between vectors, dendrogram, matrix, measure of divergence, method of grouping multiple objects
cluster analysis, cluster, function of distances between vectors, dendrogram, matrix, measure of divergence, method of grouping multiple objects
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