Ülkelerin İnsani Gelişmişlik Ölçüsüne Göre Seviyelerinin Belirlenmesinde Kümeleme Algoritmalarının Kullanılmasına İlişkin Bir Uygulama

Year 2025, Volume: 8 Issue: 1, 161 - 171, 15.01.2025

L. Sinem Sarul

https://doi.org/10.34248/bsengineering.1581168

Abstract

Temel yaşam standartlarına erişim, eğitim ve sağlıklı yaşama süresi gibi üç temel gösterge dikkate alınarak oluşturulan İnsani Gelişme İndeksi (Human Development Index) ilk olarak 1990 yılında Birleşmiş Milletler Kalkınma Programı tarafından ortaya konulmuştur. Belirtilen temel göstergeler dikkate alınarak tüm dünya ülkelerinin İnsani Gelişme İndeksi hesaplanmakta, oluşturulmuş olduğu ilk yıldan itibaren her yıl düzenli olarak Birleşmiş Milletler tarafından kamuoyu bilgisine sunulmaktadır. Bu çalışmada veri madenciliği kapsamında kümeleme analizi teknikleri detaylı olarak incelenmiş ve bu teknikler kullanılarak İnsani Gelişme İndeksine göre ülkelerin gruplaması yapılmıştır. Elde edilen sonuçlar Birleşmiş Milletler Kalkınma Programı tarafından açıklanan listeye göre karşılaştırılarak yapılan analizlerin bu alanda uygulanabilirliği tartışılmıştır.

Keywords

Veri madenciliği, Kümeleme algoritmaları, İnsani Gelişme İndeksi

An Application on the Use of Clustering Algorithms in Determining the Levels of Countries According to Their Human Development Index

Abstract

The Human Development Index, which is created by considering three basic indicators such as access to basic living standards, education and healthy life expectancy, was first created by the United Nations Development Program in 1990. The Human Development Index of all countries in the world is calculated by taking into account the specified basic indicators and is regularly presented to the public by the United Nations every year since its creation. In this study, cluster analysis techniques within the scope of data mining are examined in detail and countries are grouped according to the Human Development Index using these techniques. The results obtained are compared according to the list announced by the United Nations Development Program and the applicability of the analyses in this field was discussed.

Keywords

Data mining, Clustering algorithms, Human Development Index

References

• Berkhin P. 2006. A survey of clustering data mining techniques, In Grouping multidimensional data: Recent advances in clustering. Springer, Berlin, Germany, ss: 25-71.
• Bramer M. 2016, Principles of data mining, 3rd. ed. Springer, London,UK, ss: 221-238.
• Everitt BS. 2011. Cluster Analysis. Wiley, Londok, UK, ss: 15-110.
• Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA. 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intel, 110: 104743.
• Hair JF, Black WC, Babin BJ, Anderson RE. 2014. Multivariate statistical analysis. Pearson, New York, US, ss: 415-474.
• Han J, Kamber M, Pei J. 2023. Data mining concepts and techniques. Morgan Kaufmann Publications, San Francisco, US, ss: 379-425.
• James G, Witten D, Hastie T, Tibshirani R. 2013. An Introduction to Statistical Learning with Applications in R. Springer, New York, US, ss: 516-542.
• Kameshwaran K, Malarvizhi K. 2014. Survey on clustering techniques in data mining. Int J Comput Sci Info Technol, 5(2): 2272-2276.
• Kassambara A. 2017. Practical guide to clustering analysis in R, unsupervised machine learning. STHDA, Marseille, France, ss:17-185.
• Kurnaz B, Yüksel HM, Önder H, Tırınk C. 2022. 3-D Classification of agricultural areas of Turkey using mammalian livestock existence. BSJ Agri, 5(3): 311-313.
• MacQueen J. 1967. Some methods for classification and analysis of multivariate observations. Fifth Berkeley Symposium on Mathematical Statistics and Probability, December 27, Berkeley, US, ss: 281-297.
• Morissette L, Chartier S. 2013. The k-means clustering technique: General considerations and implementation in Mathematica. Tutor Quantit Meth Psychol, 9(1): 15-24.
• Muttaqin MFJ. 2022. Cluster analysis using k-means method to classify sumatera regency and city based on human development ındex ındicator. Nas Offic Stat, 2022: 967-976.
• Neha D, Vidyavathi BM. 2015. A survey on applications of data mining using clustering techniques. Int J Comput Appl, 126(2): 7-12.
• Nurhasanah N, Salwa N, Ornila L, Hasan A, Mardhani M. 2021. Classifying regencies and cities on human development index dimensions: Application of K-Means cluster analysis. J Sains Sosio Humaniora, 5(2): 759-765. Özkan Y. 2013. Veri madenciliği yöntemleri. Papatya Yayınclık, İstanbul, Türkiye, ss: 131-156.
• Pujari AK. 2001. Clustering techniques in data mining- A survey. JETE J Res, 47(1&2): 19-28.
• Rocha JLM, Zela MAC, Torres NIV, Medina GS. 2021. Analogy of the application of clustering and K-means techniques for the approximation of values of human development indicators. Int J Adv Comput Sci Appl, 12(9): 526-532.
• Shah M, Nair S. 2015. A survey of data mining clustering algorithms. Int J Comput Appl, 128(1): 1-5.
• Wang H, Feil JH, Yu X. 2023. Let the data speak about the cut-off values for multidimensional index: Classification of human development index with machine learning, Socio-Econ Plan Sci, 87:101523.
• Ward JH. 1963. Hierarchical grouping to optimize an objective function. J Amer Stat Assoc, 5(301): 236-244.