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Seçili Kripto Paralarda Kümeleme Analizi

Year 2020, Volume: 5 Issue: 1, 41 - 52, 30.04.2020

İlker İ. Avşar Zehra Vildan Serin

Abstract

Para, günlük hayatımızda çok önemli bir yere sahiptir. Günümüze kadar çok farklı para türleri kullanılmıştır. İçinde bulunduğumuz yüzyılda dünya çapında yaygın olarak kullanılan paralar çoğunlukla merkezi bir modelin ürünüdür. 2009 yılından itibaren dağıtık bir mimariye sahip olan blok zinciri teknolojisi ile inşa edilmiş kripto paralar finans sektöründe yerini almıştır. Nispeten yeni olan bu para birimleri konusunda pek çok tereddütler yaşanmaktadır. Kripto para kümeleme çalışmaları, kripto paralar hakkında yapılacak tahmin çalışmalarında kullanılabilecek yöntemlerden biridir. Çok değişkenli bir analiz türü olan kümeleme analizi, birbirine benzeyen verilerin sınıflandırılmasında kullanılabilir. Kümeleme analizi, bir konu ile ilgili verileri gruplama işlemidir. Kümeleme analizinde oluşan her kümenin kendi içerisinde benzerliği yüksek olmasına karşın diğer kümeler ile benzerliği düşük seviyede olmalıdır.
Bu çalışmada; piyasa değeri belirli bir büyüklüğe erişmiş (piyasa değeri açısından ilk 25’i oluşturan) kripto para fiyatlarının 1 saatlik, 24 saatlik ve 7 günlük değişim verilerine göre kümeleme yapılmıştır. Yapılan kümelemede 6 seviye oluşmuştur. En alt seviye sonucuna göre birbirine en çok benzeyen kripto paralar NEO ile Ontology olmuştur.

Keywords

Kripto para kümeleme analizi veri madenciliği

References

  • Adana Karaağaç, G.,ve Altınırmak, S. (2018). En Yüksek Piyasa Değerine Sahip On Kripto Paranın Birbirleriyle Etkileşimi. Muhasebe ve Finansman Dergisi, 123-136. doi:DOI: 10.25095/mufad.438852
  • Aggarwal, S., Chaudhary, R., Aujla, G. S., Kumar, N., Choo, K.-K. R., & Zomaya, A. Y. (2019). Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications, 144(15), 13-48. doi:https://doi.org/10.1016/j.jnca.2019.06.018
  • Akın, B. H.,ve Eren, Ö. (2012). Oecd Ülkelerinin Eğitim Göstergelerinin Kümeleme Analizi Ve Çok Boyutlu Ölçekleme Analizi İle Karşılaştırmalı Analizi. Öneri Dergisi, 175-181.
  • Al-Yahyaee, K. H., Mensi, W., Al-Jarrah, I. M., Hamdi, A.,ve Kang, S. H. (2019). Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analysis with yellow metal. The North American Journal of Economics and Finance, 49, 104-120. doi:https://doi.org/10.1016/j.najef.2019.04.001
  • Aşan, Z. (2007). Kredi Kartı Kullanan Müşterilerin Sosyo Ekonomik Özelliklerinin Kümeleme Analiziyle İncelenmesi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 17, 256-267. https://dergipark.org.tr/tr/pub/dpusbe/issue/4759/65384
  • Ballis, A.,ve Drakos, K. (2019). Testing for herding in the cryptocurrency market. Finance Research Letters, 1-5. doi:https://doi.org/10.1016/j.frl.2019.06.008
  • Berberoğlu, B. (2011). 2008 global krizinin Türkiye ve Avrupa Birliği'ndeki etkilerinin kümeleme analizi ile incelenmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 11(1), 105–130.
  • Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19. doi:https://doi.org/10.1016/j.jempfin.2018.11.002
  • Bouri, E., Roubaud, D.,ve Shahzad, S. J. (2019). Do Bitcoin and other cryptocurrencies jump together? The Quarterly Review of Economics and Finance, 1-14. doi:https://doi.org/10.1016/j.qref.2019.09.003
  • Casino, F., Dasaklis, T. K.,ve Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55-81. doi:https://doi.org/10.1016/j.tele.2018.11.006
  • Charfeddine, L., Benlagha, N.,ve Maouchi, Y. (2019). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling. doi:https://doi.org/10.1016/j.econmod.2019.05.016
  • Cirrincione, G., Ciravegna, G., Barbiero, P., Randazzo, V.,ve Pasero, E. (2020). The GH-EXIN neural network for hierarchical clustering. Neural Networks, 121, 57-73. doi:https://doi.org/10.1016/j.neunet.2019.07.018
  • Corbet, S., Cumming, D. J., Lucey, B. M., Peat, M.,ve Vigne, S. A. (2019). The destabilising effects of cryptocurrency cybercriminality. Economics Letters, 108741.
  • Dash, M., Liu, H., Scheuermann, P.,ve Tan, K. L. (2003). Fast hierarchical clustering and its validation. Datave Knowledge Engineering, 44(1), 109-138. doi:https://doi.org/10.1016/S0169-023X(02)00138-6
  • Everitt, B. S. (1979). Unresolved Problems in Cluster Analysis. Biometrics, 35(1), 169-181. https://www.jstor.org/stable/2529943 Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20(1), 9-49. doi:https://doi.org/10.1080/10864415.2016.1061413
  • Gan, G., Ma, C.,ve Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. SIAM. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C.,ve Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users' Hidden Intentions. ECIS. Tel Aviv. https://ssrn.com/abstract=2425247
  • Gil-Alana, L. A., Abakah, E. J.,ve Rojo, M. F. (2020). Cryptocurrencies and stock market indices. Are they related? Research in International Business and Finance, 51, 1-11. doi:https://doi.org/10.1016/j.ribaf.2019.101063
  • Govender, P.,ve Sivakumar, V. (2019). Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980-2019). Atmospheric Pollution Research, 1-68. doi:https://doi.org/10.1016/j.apr.2019.09.009
  • Jafarzadegan, M., Safi-Esfahani, F.,ve Beheshti, Z. (2019). Combining hierarchical clustering approaches using the PCA method. Expert Systems with Applications, 137(15), 1-10. doi:https://doi.org/10.1016/j.eswa.2019.06.064
  • Jain, A. K., Murty, M. N.,ve Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504
  • Klarin, A. (2020). The decade-long cryptocurrencies and the blockchain rollercoaster: Mapping the intellectual structure and charting future directions. Research in International Business and Finance, 1-16. doi:https://doi.org/10.1016/j.ribaf.2019.101067
  • Kindhi, B. A., Sardjono, T. A., Purnomo, M. H.,ve Verkerke, G. J. (2019). Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis. Expert Systems with Applications, 121, 373-381. doi:https://doi.org/10.1016/j.eswa.2018.12.019
  • Muzammal, M., Qu, Q.,ve Nasrulin, B. (2019). Renovating blockchain with distributed databases: An open source system. Future Generation Computer Systems, 90, 105-117. doi:https://doi.org/10.1016/j.future.2018.07.042
  • Nakamoto , S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Bitcoin: https://bitcoin.org/bitcoin.pdf
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski , R.,ve Lightfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20, 9-49. https://doi.org/10.1080/10864415.2016.1061413
  • Samarasinghe, T., Gunawardena, T., Mendis, P., Sofi, M.,ve Aye, L. (2019). Dependency Structure Matrix and Hierarchical Clustering based algorithm for optimum module identification in MEP systems. Automation in Construction, 104, 153-178. doi:https://doi.org/10.1016/j.autcon.2019.03.021
  • Sandal, E. K. (2009). Sosyo-Ekonomik Kriterler Bakımından Türkiye, Doğu Avrupa Ve Kafkas Ülkelerinin Karşılaştırılması. Doğu Coğrafya Dergisi, 14(22), 89-106.
  • Shahzad, S. J., Bouri, E., Roubaud, D., Kristoufek, L.,ve Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322-330. doi:https://doi.org/10.1016/j.irfa.2019.01.002
  • Song, J. Y., Chang, W.,ve Song, J. W. (2019). Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering. Physica A: Statistical Mechanics and its Applications, 527, 121339. doi:https://doi.org/10.1016/j.physa.2019.121339
  • Tekin, B. (2015). Temel Sağlık Göstergeleri Açısından Türkiye’deki İllerin Gruplandırılması: Bir Kümeleme Analizi Uygulaması. Çankırı Karatekin Üniversitesi İİBF Dergisi, 5(2), 389-416. https://dergipark.org.tr/tr/pub/ckuiibfd/issue/32905/365558
  • Thakur, V., Doja, M. N., Dwivedi, Y. K., Ahmad, T.,ve Khadanga, G. (2019). Land records on Blockchain for implementation of Land Titling in India. International Journal of Information Management, 1-9. doi:https://doi.org/10.1016/j.ijinfomgt.2019.04.013
  • Tiwari, A. K., Adewuyi, A. O., Albulescu, C. T.,ve Wohar, M. E. (2019). Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies. The North American Journal of Economics and Finance, 101083. doi:https://doi.org/10.1016/j.najef.2019.101083
  • Turanlı, M., Özden, Ü. H.,ve Türedi, S. (2006). Avrupa Birliği'ne aday ve üye ülkelerin ekonomik benzerliklerinin kümeleme analiziyle incelenmesi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5(9), 95-108.
  • Wang, K. (2019). Power System Critical Cutset Identification based on Rolling Double Level Hierarchical Clustering. Energy Procedia, 159, 148-153. doi:https://doi.org/10.1016/j.egypro.2018.12.033
  • Wei, W., Liang, J., Guo, X., Song, P.,ve Sun, Y. (2019). Hierarchical division clustering framework for categorical data. Neurocomputing, 341, 118-134. doi:https://doi.org/10.1016/j.neucom.2019.02.043
  • Yılmaz, Ö.,ve Temurlenk , S. M. (2005). Türkiye'deki İstatistik Bölgelerin Kışı Başına Düşen Gelir Açısından Hiyerarşik Ve Hiyerarşik Olmayan Kümeleme Analizi İle Değerlendirilmesi: 1965-2001. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(2), 75 - 92. https://dergipark.org.tr/tr/pub/atauniiibd/issue/2688/35321
  • Yu, W. (2019). A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks. Applied Soft Computing, 85, 1-18. doi:https://doi.org/10.1016/j.asoc.2019.105785
  • Zeitsch, P. J. (2019). Ajumpmodelforcreditdefaultswapswithhierarchicalclustering. Physica A: Statistical Mechanics and its Applications, 524, 737-775. doi:https://doi.org/10.1016/j.physa.2019.04.255
  • Zhang, L., Li, H., Yu, Y., Au, M. H.,ve Wang, B. (2019). An efficient linkable group signature for payer tracing in anonymous cryptocurrencies. Future Generation Computer Systems, 101, 29-38. doi:https://doi.org/10.1016/j.future.2019.05.081
  • Zięba, D., Kokoszczyński, R.,ve Śledziewska, K. (2019). Shock transmission in the cryptocurrency market. Is Bitcoin the most influential? International Review of Financial Analysis, 64, 102-125. doi:https://doi.org/10.1016/j.irfa.2019.04.009
  • İnternet Kaynakları
  • 11.14, 2019 tarihinde Coinmarketcap: https://coinmarketcap.com/
  • Hierarchical Clustering. 8 21, 2019 tarihinde rdocumentation: https://www.rdocumentation.org/packages/stats/versions/3.6.1/topics/hclust

CLUSTERING ANALYSIS OF SELECTED CRYPTOCURRENCIES

Year 2020, Volume: 5 Issue: 1, 41 - 52, 30.04.2020

İlker İ. Avşar Zehra Vildan Serin

Abstract

Money has a very important place in our daily lives. To date, many different types of money have been used. The coins commonly used throughout the world in the present century are mostly the products of a central model. Since 2009, the cryptocurrencies built with blockchain technology, which has a distributed architecture, have taken their place in the financial sector. There are many doubts about these relatively new currencies. Cryptocurrency clustering is one of the methods that can be used in forecasting studies on cryptocurrencies. In the light of this information, it is the aim of the study to give investors an idea by revealing their similarity levels in terms of price changes of selected cryptocurrencies. In this study; cryptocurrencies are clustered according to 1-hour, 24-hour and 7-day exchange data of cryptocurrency prices that have reached market value a certain size (which constitutes the top 25 in terms of market value). There are 6 levels in the clustering. According to the lowest level result, the most similar cryptocurrencies were Ontology with NEO. It is a remarkable result that the ATOM currency does not enter the same cluster with any currency at the lower levels.

References

  • Adana Karaağaç, G.,ve Altınırmak, S. (2018). En Yüksek Piyasa Değerine Sahip On Kripto Paranın Birbirleriyle Etkileşimi. Muhasebe ve Finansman Dergisi, 123-136. doi:DOI: 10.25095/mufad.438852
  • Aggarwal, S., Chaudhary, R., Aujla, G. S., Kumar, N., Choo, K.-K. R., & Zomaya, A. Y. (2019). Blockchain for smart communities: Applications, challenges and opportunities. Journal of Network and Computer Applications, 144(15), 13-48. doi:https://doi.org/10.1016/j.jnca.2019.06.018
  • Akın, B. H.,ve Eren, Ö. (2012). Oecd Ülkelerinin Eğitim Göstergelerinin Kümeleme Analizi Ve Çok Boyutlu Ölçekleme Analizi İle Karşılaştırmalı Analizi. Öneri Dergisi, 175-181.
  • Al-Yahyaee, K. H., Mensi, W., Al-Jarrah, I. M., Hamdi, A.,ve Kang, S. H. (2019). Volatility forecasting, downside risk, and diversification benefits of Bitcoin and oil and international commodity markets: A comparative analysis with yellow metal. The North American Journal of Economics and Finance, 49, 104-120. doi:https://doi.org/10.1016/j.najef.2019.04.001
  • Aşan, Z. (2007). Kredi Kartı Kullanan Müşterilerin Sosyo Ekonomik Özelliklerinin Kümeleme Analiziyle İncelenmesi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 17, 256-267. https://dergipark.org.tr/tr/pub/dpusbe/issue/4759/65384
  • Ballis, A.,ve Drakos, K. (2019). Testing for herding in the cryptocurrency market. Finance Research Letters, 1-5. doi:https://doi.org/10.1016/j.frl.2019.06.008
  • Berberoğlu, B. (2011). 2008 global krizinin Türkiye ve Avrupa Birliği'ndeki etkilerinin kümeleme analizi ile incelenmesi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 11(1), 105–130.
  • Borri, N. (2019). Conditional tail-risk in cryptocurrency markets. Journal of Empirical Finance, 50, 1-19. doi:https://doi.org/10.1016/j.jempfin.2018.11.002
  • Bouri, E., Roubaud, D.,ve Shahzad, S. J. (2019). Do Bitcoin and other cryptocurrencies jump together? The Quarterly Review of Economics and Finance, 1-14. doi:https://doi.org/10.1016/j.qref.2019.09.003
  • Casino, F., Dasaklis, T. K.,ve Patsakis, C. (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36, 55-81. doi:https://doi.org/10.1016/j.tele.2018.11.006
  • Charfeddine, L., Benlagha, N.,ve Maouchi, Y. (2019). Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling. doi:https://doi.org/10.1016/j.econmod.2019.05.016
  • Cirrincione, G., Ciravegna, G., Barbiero, P., Randazzo, V.,ve Pasero, E. (2020). The GH-EXIN neural network for hierarchical clustering. Neural Networks, 121, 57-73. doi:https://doi.org/10.1016/j.neunet.2019.07.018
  • Corbet, S., Cumming, D. J., Lucey, B. M., Peat, M.,ve Vigne, S. A. (2019). The destabilising effects of cryptocurrency cybercriminality. Economics Letters, 108741.
  • Dash, M., Liu, H., Scheuermann, P.,ve Tan, K. L. (2003). Fast hierarchical clustering and its validation. Datave Knowledge Engineering, 44(1), 109-138. doi:https://doi.org/10.1016/S0169-023X(02)00138-6
  • Everitt, B. S. (1979). Unresolved Problems in Cluster Analysis. Biometrics, 35(1), 169-181. https://www.jstor.org/stable/2529943 Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20(1), 9-49. doi:https://doi.org/10.1080/10864415.2016.1061413
  • Gan, G., Ma, C.,ve Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. SIAM. Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C.,ve Siering, M. (2014). Bitcoin - Asset or Currency? Revealing Users' Hidden Intentions. ECIS. Tel Aviv. https://ssrn.com/abstract=2425247
  • Gil-Alana, L. A., Abakah, E. J.,ve Rojo, M. F. (2020). Cryptocurrencies and stock market indices. Are they related? Research in International Business and Finance, 51, 1-11. doi:https://doi.org/10.1016/j.ribaf.2019.101063
  • Govender, P.,ve Sivakumar, V. (2019). Application of k-means and hierarchical clustering techniques for analysis of air pollution: a review (1980-2019). Atmospheric Pollution Research, 1-68. doi:https://doi.org/10.1016/j.apr.2019.09.009
  • Jafarzadegan, M., Safi-Esfahani, F.,ve Beheshti, Z. (2019). Combining hierarchical clustering approaches using the PCA method. Expert Systems with Applications, 137(15), 1-10. doi:https://doi.org/10.1016/j.eswa.2019.06.064
  • Jain, A. K., Murty, M. N.,ve Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504
  • Klarin, A. (2020). The decade-long cryptocurrencies and the blockchain rollercoaster: Mapping the intellectual structure and charting future directions. Research in International Business and Finance, 1-16. doi:https://doi.org/10.1016/j.ribaf.2019.101067
  • Kindhi, B. A., Sardjono, T. A., Purnomo, M. H.,ve Verkerke, G. J. (2019). Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis. Expert Systems with Applications, 121, 373-381. doi:https://doi.org/10.1016/j.eswa.2018.12.019
  • Muzammal, M., Qu, Q.,ve Nasrulin, B. (2019). Renovating blockchain with distributed databases: An open source system. Future Generation Computer Systems, 90, 105-117. doi:https://doi.org/10.1016/j.future.2018.07.042
  • Nakamoto , S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Bitcoin: https://bitcoin.org/bitcoin.pdf
  • Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski , R.,ve Lightfoot, G. (2015). Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry. International Journal of Electronic Commerce, 20, 9-49. https://doi.org/10.1080/10864415.2016.1061413
  • Samarasinghe, T., Gunawardena, T., Mendis, P., Sofi, M.,ve Aye, L. (2019). Dependency Structure Matrix and Hierarchical Clustering based algorithm for optimum module identification in MEP systems. Automation in Construction, 104, 153-178. doi:https://doi.org/10.1016/j.autcon.2019.03.021
  • Sandal, E. K. (2009). Sosyo-Ekonomik Kriterler Bakımından Türkiye, Doğu Avrupa Ve Kafkas Ülkelerinin Karşılaştırılması. Doğu Coğrafya Dergisi, 14(22), 89-106.
  • Shahzad, S. J., Bouri, E., Roubaud, D., Kristoufek, L.,ve Lucey, B. (2019). Is Bitcoin a better safe-haven investment than gold and commodities? International Review of Financial Analysis, 63, 322-330. doi:https://doi.org/10.1016/j.irfa.2019.01.002
  • Song, J. Y., Chang, W.,ve Song, J. W. (2019). Cluster analysis on the structure of the cryptocurrency market via Bitcoin–Ethereum filtering. Physica A: Statistical Mechanics and its Applications, 527, 121339. doi:https://doi.org/10.1016/j.physa.2019.121339
  • Tekin, B. (2015). Temel Sağlık Göstergeleri Açısından Türkiye’deki İllerin Gruplandırılması: Bir Kümeleme Analizi Uygulaması. Çankırı Karatekin Üniversitesi İİBF Dergisi, 5(2), 389-416. https://dergipark.org.tr/tr/pub/ckuiibfd/issue/32905/365558
  • Thakur, V., Doja, M. N., Dwivedi, Y. K., Ahmad, T.,ve Khadanga, G. (2019). Land records on Blockchain for implementation of Land Titling in India. International Journal of Information Management, 1-9. doi:https://doi.org/10.1016/j.ijinfomgt.2019.04.013
  • Tiwari, A. K., Adewuyi, A. O., Albulescu, C. T.,ve Wohar, M. E. (2019). Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies. The North American Journal of Economics and Finance, 101083. doi:https://doi.org/10.1016/j.najef.2019.101083
  • Turanlı, M., Özden, Ü. H.,ve Türedi, S. (2006). Avrupa Birliği'ne aday ve üye ülkelerin ekonomik benzerliklerinin kümeleme analiziyle incelenmesi. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 5(9), 95-108.
  • Wang, K. (2019). Power System Critical Cutset Identification based on Rolling Double Level Hierarchical Clustering. Energy Procedia, 159, 148-153. doi:https://doi.org/10.1016/j.egypro.2018.12.033
  • Wei, W., Liang, J., Guo, X., Song, P.,ve Sun, Y. (2019). Hierarchical division clustering framework for categorical data. Neurocomputing, 341, 118-134. doi:https://doi.org/10.1016/j.neucom.2019.02.043
  • Yılmaz, Ö.,ve Temurlenk , S. M. (2005). Türkiye'deki İstatistik Bölgelerin Kışı Başına Düşen Gelir Açısından Hiyerarşik Ve Hiyerarşik Olmayan Kümeleme Analizi İle Değerlendirilmesi: 1965-2001. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 19(2), 75 - 92. https://dergipark.org.tr/tr/pub/atauniiibd/issue/2688/35321
  • Yu, W. (2019). A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks. Applied Soft Computing, 85, 1-18. doi:https://doi.org/10.1016/j.asoc.2019.105785
  • Zeitsch, P. J. (2019). Ajumpmodelforcreditdefaultswapswithhierarchicalclustering. Physica A: Statistical Mechanics and its Applications, 524, 737-775. doi:https://doi.org/10.1016/j.physa.2019.04.255
  • Zhang, L., Li, H., Yu, Y., Au, M. H.,ve Wang, B. (2019). An efficient linkable group signature for payer tracing in anonymous cryptocurrencies. Future Generation Computer Systems, 101, 29-38. doi:https://doi.org/10.1016/j.future.2019.05.081
  • Zięba, D., Kokoszczyński, R.,ve Śledziewska, K. (2019). Shock transmission in the cryptocurrency market. Is Bitcoin the most influential? International Review of Financial Analysis, 64, 102-125. doi:https://doi.org/10.1016/j.irfa.2019.04.009
  • İnternet Kaynakları
  • 11.14, 2019 tarihinde Coinmarketcap: https://coinmarketcap.com/
  • Hierarchical Clustering. 8 21, 2019 tarihinde rdocumentation: https://www.rdocumentation.org/packages/stats/versions/3.6.1/topics/hclust
There are 43 citations in total.

Details

Primary Language Turkish
Journal Section Makaleler
Authors

İlker İ. Avşar 0000-0003-2991-380X

Zehra Vildan Serin

Publication Date April 30, 2020
Submission Date January 10, 2020
Published in Issue Year 2020Volume: 5 Issue: 1

Cite

APA Avşar, İ. İ., & Serin, Z. . V. (2020). Seçili Kripto Paralarda Kümeleme Analizi. Türk Sosyal Bilimler Araştırmaları Dergisi, 5(1), 41-52.