Clustering of Municipal Self-Government Bodies on the Basis of Statistical Indicators
Rakhimboev Khikmat Jumanazarovich1, Ismailov Mirkhalil Agzamovich2, Khusinov Khamid Khuday berganovich3

1Rakhimboev Khikmat Jumanazarovich, Senior Lecturer, Department of Information Technology, Urgench Branch of Tashkent University of Information Technologies, Uzbekistan.
2Ismailov Mirkhalil Agzamovich, Chief Researcher, Research and Innovation Center information and communication technologies, Tashkent University of information technologies, Uzbekistan.
3Khusinov Khamid Khudayberganovich, Lecturer, Urgench branch of Tashkent University of Information Technologyies, Uzbekistan.
Manuscript received on November 02, 2019. | Revised Manuscript received on November 15, 2019. | Manuscript published on December 30, 2019. | PP: 4853-4861 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3802129219/2019©BEIESP | DOI: 10.35940/ijeat.B3802.129219
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Abstract: The application of cluster analysis by the method of k – means local self-government bodies of the Republic of Uzbekistan is examined from the standpoint of the state of self-government bodies, as well as the level and quality of life of the population. Based on statistical information, the paper presents the results of the division of regions according to key indicators of self-government bodies. In the course of the study, five clusters were identified: with a low, below average, average, above average, and a high state of development of self-government. To characterize the self-governing bodies of the Khorezm region, statistics were used on self-governing bodies (population) and some system of socio-economic indicators of the level and quality of life (average per capita total income of the population, average per capita real income of the population, number of crimes and unemployment, facilities and municipal facilities , living area per inhabitant, etc.).
Keywords: level and quality of life, classification of subjects, linear transformation, multivariate statistical analysis, k-means clustering algorithm, hierarchical cluster analysis.