ASSESSMENT OF DEVELOPMENT LEVEL OF TOURISM SECTOR SERVICES IN THE FAR EASTERN FEDERAL DISTRICT BY CLUSTER ANALYSIS METHOD

2015 ◽  
Author(s):  
Михаил Третьяков ◽  
Наталья Кузнецова
Keyword(s):  
Cluster Analysis ◽  
Federal District ◽  
Analysis Method ◽  
Development Level ◽  
Tourism Sector ◽  
Cluster Analysis Method ◽  
Far Eastern

scholarly journals Evaluation and Analysis of Socio-Economic Development Level and Management in Guangxi province of China

2021 ◽  
Vol 39 (5) ◽  
Author(s):  
Li Zongkeng ◽  
Li Zhuoran ◽  
Andrii Mykhailov ◽  
Wei Shi ◽  
Yang Zhuquan ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Economic Development ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Development Level ◽  
Socio Economic Development ◽  
Cluster Analysis Method ◽  
And Cluster Analysis

This article takes 14 regions in Guangxi as the research object, selects ten indicators that can measure the level of socio-economic development, establishes an index system for evaluating the regional socio-economic development level of Guangxi regions, and uses principal component analysis method and cluster analysis method carry out comprehensive evaluation and difference analysis among the economic development level of Guangxi regions. First, the primary component analysis method uses to comprehensively evaluate the economic development level of 14 regions in Guangxi. The results show that there are vast differences in the economic development levels of regions in Guangxi. Secondly, a systematic cluster analysis method uses to classify and analyze the differences between regions according to the similarity of economic development status. Finally, combined with the results of principal component analysis and cluster analysis, comprehensive evaluation analysis and discussion on the economic development status of various regions in Guangxi, and based on the evaluation results, proposed countermeasures for the socio-economic development and management in Guangxi province of China.

scholarly journals Detection of drought events in Greece using daily precipitation

2010 ◽  
Vol 41 (2) ◽  
pp. 126-133 ◽  
Author(s):  
N. Kalamaras ◽  
H. Michalopoulou ◽  
H. R. Byun
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Daily Precipitation ◽  
Hierarchical Cluster ◽  
Seasonal Distribution ◽  
Drought Severity ◽  
Severe Drought ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
Insight Into

In this study a method proposed by Byun & Wilhite, which estimates drought severity and duration using daily precipitation values, is applied to data from stations at different locations in Greece. Subsequently, a series of indices is calculated to facilitate the detection of drought events at these sites. The results provide insight into the trend of drought severity in the region. In addition, the seasonal distribution of days with moderate and severe drought is examined. Finally, the Hierarchical Cluster Analysis method is used to identify sites with similar drought features.

Cluster Analysis Using N-gram Statistics for Daihinmin Programs and Performance Evaluations

2016 ◽  
Vol 4 (2) ◽  
pp. 33-57 ◽  
Author(s):  
Seiya Okubo ◽  
Takaaki Ayabe ◽  
Tetsuro Nishino
Keyword(s):  
Cluster Analysis ◽  
Imperfect Information ◽  
Computer Game ◽  
Computer Experiments ◽  
Performance Evaluations ◽  
Analysis Method ◽  
Cluster Analysis Method ◽  
N Gram ◽  
And Performance ◽  
Feature Values

In this paper, the authors elucidate the characteristics of the computer game Daihinmin, a popular Japanese card game that uses imperfect information. They first propose a method to extract feature values using n-gram statistics and a cluster analysis method that employs feature values. By representing the program card hands as several symbols, and the order of hands as simplified symbol strings, they obtain data that is suitable for feature extraction. The authors then evaluate the effectiveness of the proposed method through computer experiments. In these experiments, they apply their method to ten programs that were used in the UEC Computer Daihinmin Convention. In addition, the authors evaluate the robustness of the proposed method and apply it to recent programs. Finally, they show that their proposed method can successfully cluster Daihinmin programs with high probability.

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