Yıl: 2022 Cilt: 10 Sayı: 2 Sayfa Aralığı: 85 - 104 Metin Dili: İngilizce DOI: 10.17093/alphanumeric.1002461 İndeks Tarihi: 13-07-2023

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

Öz:
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 into groups according to their prosperity levels with Cluster Analysis (CA), which is one of the DM techniques, and to show the applicability of the method. In this context, the 2019 data of 167 countries within the 12 prosperity indicators in The Legatum Prosperity Index (LPI) were used. In the study, countries were divided into groups with the Ward’s algorithm and the similarities between the countries were determined with the K-Means and Turkey's place in the groups was determined. The results show that countries are divided into three clusters according to their prosperity levels. The most effective indicators in dividing them into clusters are "market access and infrastructure, education, investment environment", and the least effective indicators are "social capital, natural environment, safety and security". It has been determined that Turkey 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.
Anahtar Kelime: Data Mining Prosperity Level Legatum Prosperity Index Cluster Analysis.

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
APA Koltan Yılmaz Ş, ŞENER S (2022). Analysis of The Countries According to The Prosperity Level with Data Mining. , 85 - 104. 10.17093/alphanumeric.1002461
Chicago Koltan Yılmaz Şebnem,ŞENER Sibel Analysis of The Countries According to The Prosperity Level with Data Mining. (2022): 85 - 104. 10.17093/alphanumeric.1002461
MLA Koltan Yılmaz Şebnem,ŞENER Sibel Analysis of The Countries According to The Prosperity Level with Data Mining. , 2022, ss.85 - 104. 10.17093/alphanumeric.1002461
AMA Koltan Yılmaz Ş,ŞENER S Analysis of The Countries According to The Prosperity Level with Data Mining. . 2022; 85 - 104. 10.17093/alphanumeric.1002461
Vancouver Koltan Yılmaz Ş,ŞENER S Analysis of The Countries According to The Prosperity Level with Data Mining. . 2022; 85 - 104. 10.17093/alphanumeric.1002461
IEEE Koltan Yılmaz Ş,ŞENER S "Analysis of The Countries According to The Prosperity Level with Data Mining." , ss.85 - 104, 2022. 10.17093/alphanumeric.1002461
ISNAD Koltan Yılmaz, Şebnem - ŞENER, Sibel. "Analysis of The Countries According to The Prosperity Level with Data Mining". (2022), 85-104. https://doi.org/10.17093/alphanumeric.1002461
APA Koltan Yılmaz Ş, ŞENER S (2022). Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal, 10(2), 85 - 104. 10.17093/alphanumeric.1002461
Chicago Koltan Yılmaz Şebnem,ŞENER Sibel Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal 10, no.2 (2022): 85 - 104. 10.17093/alphanumeric.1002461
MLA Koltan Yılmaz Şebnem,ŞENER Sibel Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal, vol.10, no.2, 2022, ss.85 - 104. 10.17093/alphanumeric.1002461
AMA Koltan Yılmaz Ş,ŞENER S Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal. 2022; 10(2): 85 - 104. 10.17093/alphanumeric.1002461
Vancouver Koltan Yılmaz Ş,ŞENER S Analysis of The Countries According to The Prosperity Level with Data Mining. Alphanumeric Journal. 2022; 10(2): 85 - 104. 10.17093/alphanumeric.1002461
IEEE Koltan Yılmaz Ş,ŞENER S "Analysis of The Countries According to The Prosperity Level with Data Mining." Alphanumeric Journal, 10, ss.85 - 104, 2022. 10.17093/alphanumeric.1002461
ISNAD Koltan Yılmaz, Şebnem - ŞENER, Sibel. "Analysis of The Countries According to The Prosperity Level with Data Mining". Alphanumeric Journal 10/2 (2022), 85-104. https://doi.org/10.17093/alphanumeric.1002461