• ISSN: 2148-2225 (online)

Ulaştırma ve Lojistik Kongreleri

alphanumeric journal

The Journal of Operations Research, Statistics, Econometrics and Management Information Systems

Analysis of The Countries According to The Prosperity Level with Data Mining

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Şebnem Koltan Yılmaz, Ph.D.

Sibel Şener, Ph.D.


Abstract

Data mining (DM) includes techniques for finding meaningful information hidden in these massive data stacks. The aim of this study is to divide the countries according to their prosperity levels with Cluster Analysis (CA), which is one of the DM techniques. In this context, the 2019 data of 167 countries within the updated 12 prosperity indicators in The Legatum Prosperity Index (LPI) were used. Countries were divided into clusters with the Ward’s algorithm, and the Elbow method was used for verifying of the optimal cluster number. The similarities between the countries were determined with the K-Means, and Tukiye's place in the clusters was determined. The results show that countries are divided into three clusters. The most significant indicators in separating them into clusters are "market access and infrastructure, education, investment environment", and the least significant indicators are "social capital, natural environment, safety and security". It has been determined that Turkiye is located in the middle prosperity level cluster and its "health, living conditions, education" indicators are the highest, while its "natural environment, personal freedom, management" indicators are the lowest.

Keywords: Cluster Analysis, Data Mining, Legatum Prosperity Index, Prosperity Level

Jel Classification: C46

Suggested citation

Koltan Yılmaz, Ş. & Şener, S. (). Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal, 10(2), 85-104. https://doi.org/10.17093/alphanumeric.1002461

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Volume 10, Issue 2, 2022

2022.10.02.MIS.01

alphanumeric journal

Volume 10, Issue 2, 2022

Pages 85-104

Received: Oct. 29, 2021

Accepted: July 7, 2022

Published: Dec. 31, 2022

Full Text [559.9 KB]

2022 Koltan Yılmaz, Ş., Şener, S.

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