Abstract:
The paper considers intelligent methods of data clustering. In recent years there has been an
increase in the amount of data to be analyzed in various fields. As a result, there is a growing need for more
efficient data clustering methods. Data clustering methods can be divided into two main categories:
hierarchical and non-hierarchical. Hierarchical clustering methods build a tree of clusters, starting with each
feature in a separate cluster and then merging close clusters until there is one cluster containing all the
features. Non-hierarchical clustering methods determine the number of clusters in advance and group
objects according to their similarities and differences. Data clustering methods is one of the most important
areas of machine learning, which allows you to group data according to their features and characteristics.
Data clustering is one of the main methods of data analysis and is widely used in many fields, including
biology, medicine, economics, sociology, and others.
Citation:
R. A. Zhilov, “Intelligent data clustering methods”, News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2023, no. 6, 152–159
\Bibitem{Zhi23}
\by R.~A.~Zhilov
\paper Intelligent data clustering methods
\jour News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences
\yr 2023
\issue 6
\pages 152--159
\mathnet{http://mi.mathnet.ru/izkab730}
\crossref{https://doi.org/10.35330/1991-6639-2023-6-116-152-159}
\elib{https://elibrary.ru/item.asp?id=https://www.elibrary.ru/item.asp?id=58804975}
\edn{https://elibrary.ru/LBDSYZ}