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Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği

Year 2021, , 511 - 540, 28.04.2021

Cihan Çılgın , Aslı Seda Kurt

https://doi.org/10.17233/sosyoekonomi.2021.02.24
Cited By: 3

Abstract

Dış ticaret refahın en önemli kaynaklarından biridir. Ülkelerin nüfusları, kişi başına düşen gelir düzeyleri, ülkeler arasındaki uzaklık gibi faktörler ise dış ticaretin önemli belirleyenlerindendir. Bu çalışmanın amacı, Türkiye ve Türk Cumhuriyetleri’nin ihracat ve ithalata ilişkin 2017 yılına ait veri setlerini, dış ticaretin temel belirleyenlerini de dikkate alarak kümelemektir. Çalışmada Türkiye’nin yanı sıra veri seti açısından uygun durumda bulunan Kazakistan ve Azerbaycan ülkeleri dikkate alınmıştır. Çalışmanın amacı doğrultusunda K-ortalama, Ward hiyerarşik kümeleme ve kendi kendini düzenleyen haritalar yöntemlerinden yararlanılmıştır. Uygulama sonuçları, ele alınan ülkelerin ihracat ve ithalatındaki kümelere ilişkin detaylı tespitler sunmaktadır.

Keywords

Dış Ticaret Kümeleme K-Ortalama Ward Hiyerarşik Kümeleme Kendi Kendini Düzenleyen Haritalar (SOM)

References

  • Akal, M. & A. Karakaş (2011), “Türk Cumhuriyetlerinin Üretim Yapılarındaki Dönüşüm ve Türkiye ile Ticari İlişkileri: 1996-2005”, Bilig, Türk Dünyası Sosyal Bilimler Dergisi, 58, 1-28.
  • Aker, Ş. & I. Aghaei (2019), “Comparison of business environments in oil-rich MENA countries: a clustering analysis of economic diversification and performance”, Emerging Markets Finance and Trade, 55(12), 2871-85.
  • Akyüz, K.C. (2006), “Avrupa Birliği Sürecinde Türkiye Orman Ürünleri Sanayinin Rekabet Düzeyi”, ZKÜ Bartın Orman Fakültesi Dergisi, 8(9), 83-94.
  • Attewell, P. & D.B. Monaghan & D. Kwong (2015), Data Mining for the Social Sciences: An Introduction, Oakland, California: Univ of California Press.
  • Bação, F. & V. Lobo & M. Painho (2005), “Self-organizing Maps as Substitutes for K-means Clustering”, International Conference on Computational Science-ICSS 2005, Springer: 476-83.
  • Bircan, H. & M. Zontul & A.G. Yüksek (2006), “SOM Tipinde Yapay Sinir Ağlarını Kullanarak Türkiye’nin İhracat Yaptığı Ülkelerin Kümelenmesi Üzerine Bir Çalışma”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20, 219-39.
  • Bholowalia, P. & A. Kumar (2014), “EBK-means: A clustering technique based on elbow method and k-means in WSN”, International Journal of Computer Applications, 105(9), 17-24.
  • Bulum, A.Z. & F. Ersöz & T. Ersöz (2013), “Dünya Ticaret Örgütü (WTO) Üyesi Ülkelerin Uluslararası Ticaret Hacimleri Üzerine Bir Çalışma”, Nevşehir Bilim ve Teknoloji Dergisi, 2(2), 153-65.
  • Caliński, T. & J. Harabasz (1974), “A Dendrite Method for Cluster Analysis”, Communications in Statistics-theory and Methods, 3(1), 1-27.
  • Carter, C. (2002), Great Circle Distances, SiRF White Paper.
  • Chaudhary, V. & R.S. Bhatia & A.K. Ahlawat (2014), “A Novel Self-Organizing Map (SOM) Learning Algorithm with Nearest and Farthest Neurons”, Alexandria Engineering Journal, 53(4), 827-31.
  • Çakmak, Z. (1999), “Kümeleme Analizinde Geçerlilik Problemi ve Kümeleme Sonuçlarının Değerlendirmesi”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 3, 187-205.
  • DPT (2000), Türkiye ile Türk Cumhuriyetleri ve Bölge Ülkeleri İlişkileri Özel İhtisas Komisyonu Raporu, Ankara. Ferreira, L. & D.B. Hitchcock (2009), “A Comparison of Hierarchical Methods for Clustering Functional Data”, Communications in Statistics-Simulation and Computation, 38(9), 1925-49.
  • Gan, G. & C. Ma & J. Wu (2007), Data Clustering: Theory, Algorithms, and Applications, 20: Siam.
  • Géron, A. (2017), Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly Media, Inc.
  • Giraudel, J.L. & S. Lek (2001), “A Comparison of Self-organizing Map Algorithm and Some Conventional Statistical Methods for Ecological Community Ordination”, Ecological Modelling, 146(1-3), 329-39.
  • Giudici, P. (2003), Applied Data Mining: Statistical Methods for Business and Industry, West Sussex: Wiley.
  • Han, J. & M. Kamber & J. Pei (2011), Data Mining: Concepts and Techniques, USA: Elsevier.
  • Hastie, T. & R. Tibshirani & J. Friedman (2001), The Elements of Statistical Learning, New York: Springer Series in Statistics.
  • Hiziroglu, A. & A. Kapusuzoglu & E. Cankal (2018), “Grouping OECD countries based on energy-related variables using K-means and fuzzy clustering”, Global Approaches in Financial Economics, Banking, and Finance, Springer: 151-69.
  • Huskinson, T. & R. Lawson (2014), “Clusters of Economic Freedom”, Applied Economics Letters, 21(15), 1070-74.
  • Jain, A.K. & R.C. Dubes (1988), Algorithms for Clustering Data, Englewood Cliffs, NJ: Prentice-Hall.
  • Jain, A.K. & M.N. Murty & P.J. Flynn (1999), “Data Clustering: A Review”, ACM Computing Surveys CSUR, 31(3), 264-323.
  • Kantar, E. & B. Deviren & M. Keskin (2011), “Hierarchical Structure of Turkey’s Foreign Trade”, Physica A, 390(2011), 3454-76.
  • Kijewska, A. & A. Bluszcz (2016), “Research of Varying Levels of Greenhouse Gas Emissions in European Countries Using the K-means Method”, Atmospheric Pollution Research, 7(5), 935-44.
  • King, R.S. (2015), Cluster Analysis and Data Mining: An Introduction, Mercury Learning & Information.
  • Kodinariya, T.M. & P.R.D. Makwana (2013), “Review on Determining Number of Cluster in K-Means Clustering”, International Journal, 1(6), 90-95.
  • Kohonen, T. (1982), “Clustering, Taxonomy, and Topological Maps of Patterns”, Proceedings of the 6th International Conference on Pattern Recognition, 114-128.
  • Kohonen, T. (2001), Self-Organizing Maps, Berlin-Heidelberg: Springer.
  • Kohonen, T. & E. Oja & O. Simula & A.J.E. Visa & J. Kangas (1996), “Engineering Applications of the Self-organizing Map”, Proceedings of the IEEE, 84(10), 1358-84.
  • Kuo, R.J. & L. Ho & C.M. Hu (2002), “Integration of Self-organizing Feature Map and K-means Algorithm for Market Segmentation”, Computers & Operations Research, 29(11), 1475-93.
  • Larose, D.T. & C.D. Larose (2014), Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley & Sons.
  • MacQueen, J. (1967), “Some Methods for Classification and Analysis of Multivariate Observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281-97.
  • Mostafa, M.M. (2010), “Clustering the Ecological Footprint of Nations Using Kohonen’s Self-organizing Maps”, Expert Systems with Applications, 37(4), 2747-55.
  • Mukherjee, A. (1997), “Self-organizing Neural Network for Identification of Natural Modes”, Journal of Computing in Civil Engineering, 11(1), 74-7.
  • Murtagh, F. & P. Legendre (2014), “Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?”, Journal of Classification, 31(3), 274-95.
  • Müller, A.C. & S. Guido (2016), Introduction to Machine Learning with Python: A Guide for Data Scientists, O’Reilly Media, Inc.
  • Nuroğlu, E. (2012), Estimating Trade Flows of Turkey Using Panel Data Analysis and Neural Networks, <https://www.researchgate.net/publication/261001076_Estimating_Trade_Flows_of_ Turkey_Using_Panel_Data_Analysis_and_Neural_Networks>, 09.08.2019.
  • Nuroğlu, E. (2014), “Estimating and Forecasting Trade Flows by Panel Data Analysis and Neural Networks”, İktisat Fakültesi Mecmuası, 64(2014/1), 85-112.
  • Özdağ, M.E. & M. Yeşilkaya & Y. Çabuk (2017), “Türkiye-Almanya Mobilya Dış Ticaretinin Yapay Sinir Ağları ile Tahmini”, Bartın Orman Fakültesi Dergisi, 19(2), 136-43.
  • Öztemel, E. (2012). Yapay Sinir Ağları, Papatya Bilim.
  • Repkine, A. (2012), “How Similar are the East Asian Economies? A Cluster Analysis Perspective on Economic Cooperation in the Region”, Journal of International and Area Studies, 19(1), 27-44.
  • Syakur, M.A. & B.K. Khotimah & E.M.S. Rochman & B.D. Satoto (2018), “Integration K-means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster”, IOP Conference Series: Materials Science and Engineering, 336(1), IOP Publishing.
  • Tan, P. & M. Steinbach & V. Kumar (2006), Introduction to Data Mining. India: Pearson Education India.
  • Tunalı, D. & A. Aytekin (2017), “Türkiye Dış Ticaretinin Kümeleme Analizi ile İncelenmesi”, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 12(3), 103-16.
  • Uygur, S. (2013), “Ülkemizin Dış Ticaret ve doğrudan Yurt Dışı Yatırımlarında Tercih Ettiği Ülkeler İçin Rank Korelasyonu ve Kümeleme Analizi”, Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(2), 95-112.
  • Vesanto, J. & E. Alhoniemi (2000), “Clustering of the Self-organizing Map”, IEEE Transactions on neural networks, 11(3), 586-600.
  • Ward Jr, J.H. (1963), “Hierarchical Grouping to Optimize an Objective Function”, Journal of the American Statistical Association, 58(301), 236-44.
  • Yayar, R. & Y. Demir & Y.E. Birol (2012), Geçiş Ekonomileri Bağlamında Türkiye-Kazakistan Dış Ticaretine Uygulamalı Bir Bakış, <https://www.avekon.org/papers/490.pdf>, 09.08.2019.
  • Yurdakul, E.M. (2014), “Türkiye’de İthalatın Gelişimi ve İthalatın Yapay Sinir Ağları Yöntemi ile Tahmin Edilebilirliğine Yönelik Bir Analiz”, Doktora Tezi, Aydın Adnan Menderes Üniversitesi.
  • Zaki, M.J. & W. Meira Jr. (2014), Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press.
  • Zontul, M. & O. Kaynar & H. Bircan (2004), “SOM Tipinde Yapay Sinir Ağlarını Kullanarak Türkiye’nin İthalat Yaptığı Ülkelerin Kümelenmesi Üzerine Bir Çalışma”, C.Ü. İktisadi ve İdari Bilimler Dergisi, 5(2), 47-68.

Cluster Analysis for Foreign Trade Data: The Case of Turkey, Azerbaijan, and Kazakhstan

Year 2021, , 511 - 540, 28.04.2021

Cihan Çılgın , Aslı Seda Kurt

https://doi.org/10.17233/sosyoekonomi.2021.02.24
Cited By: 3

Abstract

Foreign trade is one of the most critical sources of welfare. Factors such as population, per capita income, and the distance between countries is among the crucial determinants of foreign trade. This paper aims to cluster the data set regarding the export and import of Turkey and Turkic Republics by considering other determinants of foreign trade for 2017. In this study, Kazakhstan and Azerbaijan, of which data sets are available, are considered, and Turkey. For this paper, K-means, Ward hierarchical clustering, and self-organizing maps are used. The findings of this paper present detailed evidence as to the export and import of the countries handled.

Keywords

Foreign Trade Clustering K-Means Ward Hierarchical Clustering Self-Organizing Maps (SOM)

References

  • Akal, M. & A. Karakaş (2011), “Türk Cumhuriyetlerinin Üretim Yapılarındaki Dönüşüm ve Türkiye ile Ticari İlişkileri: 1996-2005”, Bilig, Türk Dünyası Sosyal Bilimler Dergisi, 58, 1-28.
  • Aker, Ş. & I. Aghaei (2019), “Comparison of business environments in oil-rich MENA countries: a clustering analysis of economic diversification and performance”, Emerging Markets Finance and Trade, 55(12), 2871-85.
  • Akyüz, K.C. (2006), “Avrupa Birliği Sürecinde Türkiye Orman Ürünleri Sanayinin Rekabet Düzeyi”, ZKÜ Bartın Orman Fakültesi Dergisi, 8(9), 83-94.
  • Attewell, P. & D.B. Monaghan & D. Kwong (2015), Data Mining for the Social Sciences: An Introduction, Oakland, California: Univ of California Press.
  • Bação, F. & V. Lobo & M. Painho (2005), “Self-organizing Maps as Substitutes for K-means Clustering”, International Conference on Computational Science-ICSS 2005, Springer: 476-83.
  • Bircan, H. & M. Zontul & A.G. Yüksek (2006), “SOM Tipinde Yapay Sinir Ağlarını Kullanarak Türkiye’nin İhracat Yaptığı Ülkelerin Kümelenmesi Üzerine Bir Çalışma”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 20, 219-39.
  • Bholowalia, P. & A. Kumar (2014), “EBK-means: A clustering technique based on elbow method and k-means in WSN”, International Journal of Computer Applications, 105(9), 17-24.
  • Bulum, A.Z. & F. Ersöz & T. Ersöz (2013), “Dünya Ticaret Örgütü (WTO) Üyesi Ülkelerin Uluslararası Ticaret Hacimleri Üzerine Bir Çalışma”, Nevşehir Bilim ve Teknoloji Dergisi, 2(2), 153-65.
  • Caliński, T. & J. Harabasz (1974), “A Dendrite Method for Cluster Analysis”, Communications in Statistics-theory and Methods, 3(1), 1-27.
  • Carter, C. (2002), Great Circle Distances, SiRF White Paper.
  • Chaudhary, V. & R.S. Bhatia & A.K. Ahlawat (2014), “A Novel Self-Organizing Map (SOM) Learning Algorithm with Nearest and Farthest Neurons”, Alexandria Engineering Journal, 53(4), 827-31.
  • Çakmak, Z. (1999), “Kümeleme Analizinde Geçerlilik Problemi ve Kümeleme Sonuçlarının Değerlendirmesi”, Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 3, 187-205.
  • DPT (2000), Türkiye ile Türk Cumhuriyetleri ve Bölge Ülkeleri İlişkileri Özel İhtisas Komisyonu Raporu, Ankara. Ferreira, L. & D.B. Hitchcock (2009), “A Comparison of Hierarchical Methods for Clustering Functional Data”, Communications in Statistics-Simulation and Computation, 38(9), 1925-49.
  • Gan, G. & C. Ma & J. Wu (2007), Data Clustering: Theory, Algorithms, and Applications, 20: Siam.
  • Géron, A. (2017), Hands-on Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, O’Reilly Media, Inc.
  • Giraudel, J.L. & S. Lek (2001), “A Comparison of Self-organizing Map Algorithm and Some Conventional Statistical Methods for Ecological Community Ordination”, Ecological Modelling, 146(1-3), 329-39.
  • Giudici, P. (2003), Applied Data Mining: Statistical Methods for Business and Industry, West Sussex: Wiley.
  • Han, J. & M. Kamber & J. Pei (2011), Data Mining: Concepts and Techniques, USA: Elsevier.
  • Hastie, T. & R. Tibshirani & J. Friedman (2001), The Elements of Statistical Learning, New York: Springer Series in Statistics.
  • Hiziroglu, A. & A. Kapusuzoglu & E. Cankal (2018), “Grouping OECD countries based on energy-related variables using K-means and fuzzy clustering”, Global Approaches in Financial Economics, Banking, and Finance, Springer: 151-69.
  • Huskinson, T. & R. Lawson (2014), “Clusters of Economic Freedom”, Applied Economics Letters, 21(15), 1070-74.
  • Jain, A.K. & R.C. Dubes (1988), Algorithms for Clustering Data, Englewood Cliffs, NJ: Prentice-Hall.
  • Jain, A.K. & M.N. Murty & P.J. Flynn (1999), “Data Clustering: A Review”, ACM Computing Surveys CSUR, 31(3), 264-323.
  • Kantar, E. & B. Deviren & M. Keskin (2011), “Hierarchical Structure of Turkey’s Foreign Trade”, Physica A, 390(2011), 3454-76.
  • Kijewska, A. & A. Bluszcz (2016), “Research of Varying Levels of Greenhouse Gas Emissions in European Countries Using the K-means Method”, Atmospheric Pollution Research, 7(5), 935-44.
  • King, R.S. (2015), Cluster Analysis and Data Mining: An Introduction, Mercury Learning & Information.
  • Kodinariya, T.M. & P.R.D. Makwana (2013), “Review on Determining Number of Cluster in K-Means Clustering”, International Journal, 1(6), 90-95.
  • Kohonen, T. (1982), “Clustering, Taxonomy, and Topological Maps of Patterns”, Proceedings of the 6th International Conference on Pattern Recognition, 114-128.
  • Kohonen, T. (2001), Self-Organizing Maps, Berlin-Heidelberg: Springer.
  • Kohonen, T. & E. Oja & O. Simula & A.J.E. Visa & J. Kangas (1996), “Engineering Applications of the Self-organizing Map”, Proceedings of the IEEE, 84(10), 1358-84.
  • Kuo, R.J. & L. Ho & C.M. Hu (2002), “Integration of Self-organizing Feature Map and K-means Algorithm for Market Segmentation”, Computers & Operations Research, 29(11), 1475-93.
  • Larose, D.T. & C.D. Larose (2014), Discovering Knowledge in Data: An Introduction to Data Mining, John Wiley & Sons.
  • MacQueen, J. (1967), “Some Methods for Classification and Analysis of Multivariate Observations”, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281-97.
  • Mostafa, M.M. (2010), “Clustering the Ecological Footprint of Nations Using Kohonen’s Self-organizing Maps”, Expert Systems with Applications, 37(4), 2747-55.
  • Mukherjee, A. (1997), “Self-organizing Neural Network for Identification of Natural Modes”, Journal of Computing in Civil Engineering, 11(1), 74-7.
  • Murtagh, F. & P. Legendre (2014), “Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?”, Journal of Classification, 31(3), 274-95.
  • Müller, A.C. & S. Guido (2016), Introduction to Machine Learning with Python: A Guide for Data Scientists, O’Reilly Media, Inc.
  • Nuroğlu, E. (2012), Estimating Trade Flows of Turkey Using Panel Data Analysis and Neural Networks, <https://www.researchgate.net/publication/261001076_Estimating_Trade_Flows_of_ Turkey_Using_Panel_Data_Analysis_and_Neural_Networks>, 09.08.2019.
  • Nuroğlu, E. (2014), “Estimating and Forecasting Trade Flows by Panel Data Analysis and Neural Networks”, İktisat Fakültesi Mecmuası, 64(2014/1), 85-112.
  • Özdağ, M.E. & M. Yeşilkaya & Y. Çabuk (2017), “Türkiye-Almanya Mobilya Dış Ticaretinin Yapay Sinir Ağları ile Tahmini”, Bartın Orman Fakültesi Dergisi, 19(2), 136-43.
  • Öztemel, E. (2012). Yapay Sinir Ağları, Papatya Bilim.
  • Repkine, A. (2012), “How Similar are the East Asian Economies? A Cluster Analysis Perspective on Economic Cooperation in the Region”, Journal of International and Area Studies, 19(1), 27-44.
  • Syakur, M.A. & B.K. Khotimah & E.M.S. Rochman & B.D. Satoto (2018), “Integration K-means Clustering Method and Elbow Method for Identification of the Best Customer Profile Cluster”, IOP Conference Series: Materials Science and Engineering, 336(1), IOP Publishing.
  • Tan, P. & M. Steinbach & V. Kumar (2006), Introduction to Data Mining. India: Pearson Education India.
  • Tunalı, D. & A. Aytekin (2017), “Türkiye Dış Ticaretinin Kümeleme Analizi ile İncelenmesi”, Eskişehir Osmangazi Üniversitesi İİBF Dergisi, 12(3), 103-16.
  • Uygur, S. (2013), “Ülkemizin Dış Ticaret ve doğrudan Yurt Dışı Yatırımlarında Tercih Ettiği Ülkeler İçin Rank Korelasyonu ve Kümeleme Analizi”, Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 32(2), 95-112.
  • Vesanto, J. & E. Alhoniemi (2000), “Clustering of the Self-organizing Map”, IEEE Transactions on neural networks, 11(3), 586-600.
  • Ward Jr, J.H. (1963), “Hierarchical Grouping to Optimize an Objective Function”, Journal of the American Statistical Association, 58(301), 236-44.
  • Yayar, R. & Y. Demir & Y.E. Birol (2012), Geçiş Ekonomileri Bağlamında Türkiye-Kazakistan Dış Ticaretine Uygulamalı Bir Bakış, <https://www.avekon.org/papers/490.pdf>, 09.08.2019.
  • Yurdakul, E.M. (2014), “Türkiye’de İthalatın Gelişimi ve İthalatın Yapay Sinir Ağları Yöntemi ile Tahmin Edilebilirliğine Yönelik Bir Analiz”, Doktora Tezi, Aydın Adnan Menderes Üniversitesi.
  • Zaki, M.J. & W. Meira Jr. (2014), Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press.
  • Zontul, M. & O. Kaynar & H. Bircan (2004), “SOM Tipinde Yapay Sinir Ağlarını Kullanarak Türkiye’nin İthalat Yaptığı Ülkelerin Kümelenmesi Üzerine Bir Çalışma”, C.Ü. İktisadi ve İdari Bilimler Dergisi, 5(2), 47-68.
There are 52 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Cihan Çılgın 0000-0002-8983-118X

Aslı Seda Kurt 0000-0003-0356-7443

Publication Date April 28, 2021
Submission Date August 24, 2020
Published in Issue Year 2021

Cite

APA Çılgın, C., & Kurt, A. S. (2021). Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği. Sosyoekonomi, 29(48), 511-540. https://doi.org/10.17233/sosyoekonomi.2021.02.24
AMA Çılgın C, Kurt AS. Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği. Sosyoekonomi. April 2021;29(48):511-540. doi:10.17233/sosyoekonomi.2021.02.24
Chicago Çılgın, Cihan, and Aslı Seda Kurt. “Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan Ve Kazakistan Örneği”. Sosyoekonomi 29, no. 48 (April 2021): 511-40. https://doi.org/10.17233/sosyoekonomi.2021.02.24.
EndNote Çılgın C, Kurt AS (April 1, 2021) Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği. Sosyoekonomi 29 48 511–540.
IEEE C. Çılgın and A. S. Kurt, “Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği”, Sosyoekonomi, vol. 29, no. 48, pp. 511–540, 2021, doi: 10.17233/sosyoekonomi.2021.02.24.
ISNAD Çılgın, Cihan - Kurt, Aslı Seda. “Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan Ve Kazakistan Örneği”. Sosyoekonomi 29/48 (April 2021), 511-540. https://doi.org/10.17233/sosyoekonomi.2021.02.24.
JAMA Çılgın C, Kurt AS. Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği. Sosyoekonomi. 2021;29:511–540.
MLA Çılgın, Cihan and Aslı Seda Kurt. “Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan Ve Kazakistan Örneği”. Sosyoekonomi, vol. 29, no. 48, 2021, pp. 511-40, doi:10.17233/sosyoekonomi.2021.02.24.
Vancouver Çılgın C, Kurt AS. Dış Ticaret Verileri İçin Kümeleme Analizi: Türkiye, Azerbaycan ve Kazakistan Örneği. Sosyoekonomi. 2021;29(48):511-40.

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