title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ SV-Ð¿Ð¾Ð´Ñ Ð¾Ð´Ð° Ðº ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Investigation of the results of applying the SV approach to solving the clustering problem
Creators: name: ÐÑÑÐ±Ð°ÑÐ¾Ð², Ð Ð¾Ð¼Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ
Ð¼ÐµÑÐ¾Ð´ k ÑÑÐµÐ´Ð½Ð¸Ñ
sv-ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ
clustering
k-means
support vector clustering

description: Ð ÐºÐ¾Ð½ÑÐµ XX Ð²ÐµÐºÐ° Ð. Ð. ÐÐ°Ð¿Ð½Ð¸ÐºÐ¾Ð¼ Ð±ÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐµÐ¾ÑÐ¸Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ð»ÐµÐ¶Ð°ÑÐ°Ñ Ð² Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð¿Ð¾ÑÐ½ÑÑ Ð²ÐµÐºÑÐ¾ÑÐ¾Ð². ÐÐ°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÐ°ÑÑÐ¾ SV-Ð¼Ð°ÑÐ¸Ð½Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸ Ð²Ð¾ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¸Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸. Ð Ð½Ð°ÑÐ°Ð»Ðµ XXI Ð²ÐµÐºÐ° Ð±ÑÐ»Ð¾ Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð¾ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ ÑÑÐ¾Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°ÑÐ¸ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÑÐ¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð¸ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ ÐµÐ³Ð¾ Ñ ÑÐ¾ÑÐ¾ÑÐ¾ Ð¸Ð·Ð²ÐµÑÑÐ½ÑÐ¼ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð¼ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ k ÑÑÐµÐ´Ð½Ð¸Ñ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ SV-ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð±ÑÐ» ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð½Ð° ÑÐ·ÑÐºÐµ R. Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð±ÑÐ»Ð° Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð½Ð° Ð¼Ð¾Ð´ÐµÐ»ÑÐ½ÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð° Ð·Ð°ÑÐµÐ¼ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÑ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¸Ð· ÑÐµÐ¿Ð¾Ð·Ð¸ÑÐ¾ÑÐ¸Ñ. Ð ÐµÐ·ÑÐ»ÑÑÐ°ÑÑ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°Ð»Ð¸ÑÑ Ñ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ°Ð¼Ð¸, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐ¼Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° k ÑÑÐµÐ´Ð½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ñ ÑÐ¸Ð»ÑÐ½ÑÐµ Ð¸ ÑÐ»Ð°Ð±ÑÐµ ÑÑÐ¾ÑÐ¾Ð½Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° SV-ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¿Ð¾Ð»ÐµÐ·Ð½Ñ Ð¿ÑÐ¸ Ð²ÑÐ±Ð¾ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸, Ð° ÑÐ°ÐºÐ¶Ðµ ÐºÐ°Ðº Ð¾ÑÐ½Ð¾Ð²Ð° Ð´Ð»Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ Ð½Ð° ÑÑÑ ÑÐµÐ¼Ñ.
description: At the end of the 20th century, V. N. Vapnik proposed a statistical theory of learning, which underlies the support vector method. Most often, SV-machines are used to solve the problems of classification and regression. At the beginning of the XXI century, it was proposed to use this approach to solve the problem of clustering. This given work is devoted to studying this approach and its comparison with the well-known clustering algorithm of k-means. During the work, the SV-clustering algorithm was implemented in the R language. The implementation was tested on model data, and then data from the repository was clustered using it. The clustering results were compared with the results obtained using the k-means algorithm. As a result, the strengths and weaknesses of the SV clustering algorithm were discovered. The results can be useful in choosing a clustering algorithm, as well as a basis for further research on this topic.