Intelligent data clustering methods

R.A. Zhilov

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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.

Keywords: data clustering, k-means method, DBSCAN method, density-based clustering methods, SOM method

For citation. Zhilov R.A. Intelligent data clustering methods. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 6(116). Pp. 152–159. DOI: 10.35330/1991-6639-2023-6-116-152-159

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Information about the author

Zhilov Ruslan Alberdovich, Junior Researcher of the Department of Neuroinformatics and Machine Learning, Institute of Applied Mathematics and Automation – branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;
360000, Russia, Nalchik, 89 A Shortanov street;
zhilov91@gmail.com, ORCID: https://orcid.org/0000-0002-3552-4854