{"id":10307,"date":"2026-04-06T16:57:09","date_gmt":"2026-04-06T15:57:09","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=10307"},"modified":"2026-04-10T08:26:22","modified_gmt":"2026-04-10T07:26:22","slug":"25-6-16-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-16-en\/","title":{"rendered":"25.6.16 En"},"content":{"rendered":"\n

Intelligent data clustering methods<\/strong><\/h1>\n\n\n\n

R.A. Zhilov<\/strong><\/p>\n\n\n\n

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Upload the full text<\/strong><\/strong><\/p>\n\n\n\n

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Abstract<\/strong>:<\/strong> <\/em>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.<\/p>\n\n\n\n

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Keywords<\/strong>:<\/em><\/strong> data clustering, k-means method, DBSCAN method, density-based clustering methods, SOM method<\/p>\n\n\n\n

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For citation<\/strong>.<\/strong> Zhilov R.A. Intelligent data clustering methods. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2023. No. 6(116). Pp. 152\u2013159. DOI: 10.35330\/1991-6639-2023-6-116-152-159<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  1. Osovsky S. Neyronnyye seti dlya obrabotki informatsii<\/em> [Neural networks for information processing]. Moscow: Finansy i statistika, 2016. (In Russian)<\/li>\n\n\n\n
  2. Mandel I.D. Klasternyy analiz <\/em>[Cluster analysis]. Moscow: Finansy i statistika, 1988. 176 p. (In Russian)<\/li>\n\n\n\n
  3. Raghavan R. A fast and scalable hardware architecture for K-means clustering for big data analysis : University of Colorado Colorado Springs<\/em>. Kraemer Family Library, 2016.<\/li>\n\n\n\n
  4. Kriegel H.-P., Schubert E., Zimek A. The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowledge and Information Systems<\/em>. 2016. Vol. 52. No. 2. P. 341.<\/li>\n\n\n\n
  5. Kohonen T. Self-Organizing Maps (Third Extended Edition). New York, 2001. 501 p.<\/li>\n\n\n\n
  6. Vyatchenin D.A. Nechotkiye metody avtomaticheskoy klassifikatsii<\/em> [Fuzzy methods of automatic classification]. Minsk: Technoprint, 2004. 219 p. (In Russian)<\/li>\n\n\n\n
  7. Zhilov R.A. Application of neural networks in data clustering. News of the KabardinoBalkarian Scientific Center of RAS.<\/em> 2021. No. 1(99). Pp. 15\u201319. (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong><\/summary>\n
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    Zhilov Ruslan Alberdovich<\/strong>, Junior Researcher of the Department of Neuroinformatics and Machine Learning, Institute of Applied Mathematics and Automation \u2013 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<\/p>\n\n\n\n

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    Intelligent data clustering methods R.A. Zhilov Upload the full text 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-10307","page","type-page","status-publish","hentry"],"yoast_head":"\n25.6.16 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-6-16-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"25.6.16 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Intelligent data clustering methods R.A. Zhilov Upload the full text 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. 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