IDEAS home Printed from https://ideas.repec.org/a/anm/alpnmr/v10y2022i2p85-104.html
   My bibliography  Save this article

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

Author

Listed:
  • Şebnem Koltan Yılmaz
  • Sibel Şener

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.

Suggested Citation

  • Şebnem Koltan Yılmaz & Sibel Şener, 2022. "Analysis of The Countries According to The Prosperity Level with Data Mining," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 85-104, December.
  • Handle: RePEc:anm:alpnmr:v:10:y:2022:i:2:p:85-104
    DOI: https://doi.org/10.17093/alphanumeric.1002461
    as

    Download full text from publisher

    File URL: https://www.alphanumericjournal.com/media/Issue/volume-10-issue-2-2022/analysis-of-the-countries-according-to-the-prosperity-level_hz6I5mI.pdf
    Download Restriction: no

    File URL: https://alphanumericjournal.com/article/analysis-of-the-countries-according-to-the-prosperity-level-with-data-mining
    Download Restriction: no

    File URL: https://libkey.io/https://doi.org/10.17093/alphanumeric.1002461?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chunhui Yuan & Haitao Yang, 2019. "Research on K-Value Selection Method of K-Means Clustering Algorithm," J, MDPI, vol. 2(2), pages 1-10, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xie, Hailun & Eames, Matt & Mylona, Anastasia & Davies, Hywel & Challenor, Peter, 2024. "Creating granular climate zones for future-proof building design in the UK," Applied Energy, Elsevier, vol. 357(C).
    2. Yunhwan Kim, 2023. "Exploring Organizational Self-(re)presentations on Visual Social Media: Computational Analysis of Startups’ Instagram Photos Based on Unsupervised Learning," SAGE Open, , vol. 13(4), pages 21582440231, December.
    3. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    4. Chao Yan & Yao Yan & Zhiyu Wan & Ziqi Zhang & Larsson Omberg & Justin Guinney & Sean D. Mooney & Bradley A. Malin, 2022. "A Multifaceted benchmarking of synthetic electronic health record generation models," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Cuomo, Maria Teresa & Tortora, Debora & Colosimo, Ivan & Ricciardi Celsi, Lorenzo & Genovino, Cinzia & Festa, Giuseppe & La Rocca, Michele, 2023. "Segmenting with big data analytics and Python: A quantitative exploratory analysis of household savings," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Zhang, Yanquan & Chang, Ruidong & Zuo, Jian & Shabunko, Veronika & Zheng, Xian, 2023. "Regional disparity of residential solar panel diffusion in Australia: The roles of socio-economic factors," Renewable Energy, Elsevier, vol. 206(C), pages 808-819.
    7. Yao, S. & Peralta-Braz, P. & Alamdari, M.M. & Ruiz, R.O. & Atroshchenko, E., 2024. "Optimal design of piezoelectric energy harvesters for bridge infrastructure: Effects of location and traffic intensity on energy production," Applied Energy, Elsevier, vol. 355(C).
    8. Yen, Barbara T.H. & Li, Jun-Sheng, 2022. "Route-based performance evaluation for airlines – A metafrontier data envelopment analysis approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    9. Yunhwan Kim, 2022. "#Nomask on Instagram: Exploring Visual Representations of the Antisocial Norm on Social Media," IJERPH, MDPI, vol. 19(11), pages 1-14, June.
    10. Yunhwan Kim & Sunmi Lee, 2022. "#ShoutYourAbortion on Instagram: Exploring the Visual Representation of Hashtag Movement and the Public’s Responses," SAGE Open, , vol. 12(2), pages 21582440221, April.
    11. Zhang, Ying & Robu, Valentin & Cremers, Sho & Norbu, Sonam & Couraud, Benoit & Andoni, Merlinda & Flynn, David & Poor, H. Vincent, 2024. "Modelling the formation of peer-to-peer trading coalitions and prosumer participation incentives in transactive energy communities," Applied Energy, Elsevier, vol. 355(C).
    12. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
    13. Ghaemi, Zahra & Tran, Thomas T.D. & Smith, Amanda D., 2022. "Comparing classical and metaheuristic methods to optimize multi-objective operation planning of district energy systems considering uncertainties," Applied Energy, Elsevier, vol. 321(C).
    14. Chen, Hao, 2022. "Cluster-based ensemble learning for wind power modeling from meteorological wind data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    15. Xinghua Wang & Xixian Liu & Fucheng Zhong & Zilv Li & Kaiguo Xuan & Zhuoli Zhao, 2023. "A Scenario Generation Method for Typical Operations of Power Systems with PV Integration Considering Weather Factors," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    16. Hazem Noori Abdulrazzak & Goh Chin Hock & Nurul Asyikin Mohamed Radzi & Nadia M. L. Tan & Chiew Foong Kwong, 2022. "Modeling and Analysis of New Hybrid Clustering Technique for Vehicular Ad Hoc Network," Mathematics, MDPI, vol. 10(24), pages 1-27, December.

    More about this item

    Keywords

    Cluster Analysis; Data Mining; Legatum Prosperity Index; Prosperity Level;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:anm:alpnmr:v:10:y:2022:i:2:p:85-104. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bahadir Fatih Yildirim (email available below). General contact details of provider: https://www.alphanumericjournal.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.