title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸Ð·Ð¼ÐµÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°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° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Study of clustering methods for measuring information
Creators: name: ÐÐ¾Ð»ÑÑÐ¸ÐºÐ¾Ð², ÐÐ¸ÑÐ°Ð»Ð¸Ð¹
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ÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ñ Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ
ÑÑÐ°ÑÐ¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ñ Ð°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸
DBSCAN
ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸ÐµÑÑ ÐºÐ°ÑÑÑ ÐÐ¾Ñ Ð¾Ð½ÐµÐ½Ð°
Ð¿Ð¾Ð³ÑÐµÑÐ½Ð¾ÑÑÑ Ð¸Ð·Ð¼ÐµÑÐµÐ½Ð¸Ð¹
clustering
metrological characteristics
algorithm
statistical characteristics
Kohonen self-organizing maps
measurement error

description: Ð¢ÐµÐ¼Ð° Ð²ÑÐ¿ÑÑÐºÐ½Ð¾Ð¹ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ: Â«ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸Ð·Ð¼ÐµÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸Â». ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐµÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸Ð·Ð¼ÐµÑÐ¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸ Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¾Ð±Ð¾Ð±ÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼ÐµÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸ Ð¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ¾ Ð¸ÑÐ¾Ð³Ð°Ð¼ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¾ ÑÑÑÐ°Ð½Ð¾Ð²Ð»ÐµÐ½Ð¾, ÑÑÐ¾ Ð½Ð° Ð¿ÑÐ°ÐºÑÐ¸ÐºÐµ ÑÐ°ÑÐµ Ð²ÑÐµÐ³Ð¾ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¾Ð¸ÑÑÐ¾Ð´Ð¸Ñ Ð±ÐµÐ· ÑÐ¾Ð³Ð»Ð°ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ñ Ð¼ÐµÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¼Ð¸ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ°Ð¼Ð¸. ÐÐ°Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐ¾ÑÑÐµÐ»ÑÑÑÐ²Ð¾ ÑÐ²ÐµÐ»Ð¸ÑÐ¸Ð²Ð°ÐµÑ ÑÐ¸ÑÐº Ð½ÐµÐ¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÐºÐ»Ð°ÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð°. ÐÑÐ»Ð° Ð¿ÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½Ð° ÑÐµÐ¾ÑÐµÑÐ¸ÑÐµÑÐºÐ°Ñ Ð¾ÑÐ½Ð¾Ð²Ð° Ð¼ÐµÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸ Ð¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸, Ð³Ð»Ð°Ð²Ð½ÑÐ¼ Ð¿ÑÐµÐ¸Ð¼ÑÑÐµÑÑÐ²Ð¾Ð¼ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ ÑÐ²Ð»ÑÐµÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ ÑÐ°Ð·Ð½ÑÐµ ÑÐ¶Ðµ Ð¸Ð·Ð²ÐµÑÑÐ½ÑÐµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸, Ð±ÐµÐ· Ð²ÑÑÐºÐ¾Ð¹ Ð¸Ñ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸. ÐÐ¿ÑÐ¾Ð±Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð° Ð½Ð° Ð¿ÑÐ¸Ð¼ÐµÑÐµ Ð¿Ð»Ð¾ÑÐ½Ð¾ÑÑÐ½Ð¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ DBSCAN Ð½Ð° ÑÐ·ÑÐºÐµ Python. ÐÑÐ´ÐµÐ»ÑÐ½ÑÐ¼ Ð¿ÑÐ½ÐºÑÐ¾Ð¼ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐ»Ð¾ÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸ÑÑÑ ÐºÐ°ÑÑ ÐÐ¾ÑÐ¾Ð½ÐµÐ½Ð° Ð² ÑÐ¾ÑÑÐ°Ð²Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ð¼ÐµÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸ Ð¾Ð±Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½Ð¾Ð¹ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸. ÐÐ¾ Ð¸ÑÐ¾Ð³Ñ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ° Ð½Ð° ÑÐ·ÑÐºÐµ Python Ð´Ð»Ñ ÑÐ´Ð¾Ð±Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ñ ÑÐ°Ð¼Ð¾Ð¾ÑÐ³Ð°Ð½Ð¸Ð·ÑÑÑÐ¸Ð¼Ð¸ÑÑ ÐºÐ°ÑÑÐ°Ð¼Ð¸ ÐÐ¾ÑÐ¾Ð½ÐµÐ½Ð° Ð¸ Ð¸Ñ Ð²Ð¸Ð·ÑÐ°Ð»Ð¸Ð·Ð°ÑÐ¸ÐµÐ¹.
description: The subject of the graduate qualification work is âStudy of clustering methods for measuring informationâ. The given work is devoted to studying features of clustering algorithms in the processing of measurement information, and to developing a generalized algorithm for metrologically reasonable clustering. According to the results of the work, it has been found that in practice, most frequently, data clustering occurs without coordination with metrological characteristics. This circumstance increases the risk of unjustified assessment of cluster analysis results. The theoretical basis of metrologically reasonable clustering has been proposed, the main advantage of which is the ability to use various already known clustering algorithms without any modification. The approbation of this approach has been carried out on the example of the density-based clustering algorithm DBSCAN in Python. A separate item of the work has been the study of the possibility of using Kohonen self-organizing maps as part of the algorithm of metrologically reasonable clustering. As a result of the research, a Python library has been implemented for convenient work with Kohonen self-organizing maps and their visualization.