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Year 2022, Volume: 43 Issue: 1, 146 - 164, 30.03.2022

Muhammet Oğuzhan Yalçın , Nevin Güler Dincer , Öznur İşçi Güneri

https://doi.org/10.17776/csj.969445

Abstract

References

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  • [2] Zarikas V, Poulopous SG, Gareiou Z, Zervas E, Clustering Analysis of the Countries COVID19 Data Sets, Data in Brief, (2020) 31.
  • [3] Mahmoudi MR, Baleanu D, Mansor Z, Tuan BA, Pho K, Fuzzy Clustering Method to Compare The Spread Rate of COVID19 in The High-risk Countries, Chaos, Solitions and Fractals, (2020) 140.
  • [4] Alvarez E, Brida JG, Limas E, Comparisions of COVID19 dynamics in the different countries of the World using time series clustering, (2020), medRxiv.
  • [5] Hutagalung J, Ginantra NLWSR, Bhawika GW, Parwita WGS, Wanto A, Panjaitan PD. COVID19 Cases and Deaths in Southeast Asia Clustering Using K-means Clustering, Annual Conference on Science and Technology Research, Journal of Physics: Conference Series, (2021) 1783.
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  • [7] Rojas F, Valenzuela O, Rojas I,. Estimation of COVID19 dynamics in the different states of the United States using time series clustering, (2020), medRxiv.
  • [8] Azarafza M, Azarafza M, Akgün H, Clustering Method for Spread Pattern Analysis of Corona-virus (COVID19) Infection in Iran, Journal of Applied Science, Engineering, Technology, and Education, 3(1) (2021).
  • [9] Crnogorac V, Grbic M, Dukanovic M, Matic D, Clustering of European countries and territories based on cumulative relative number of COVID19 patients in 2020, 20th International Symposium INFOTEH, (2021).
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  • [26]Bezdek JC, Numerical taxonomy with fuzzy sets, Journal of Mathematical Biology, 1(1) (1974) 57-71.
  • [27] Li CS, The Improved Partition Coefficient, Procedia Engineering, 24 (2011) 534-538.
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Investigating the COVID19 Characteristics of the Countries Based on Time Series Clustering

Year 2022, Volume: 43 Issue: 1, 146 - 164, 30.03.2022

Muhammet Oğuzhan Yalçın , Nevin Güler Dincer , Öznur İşçi Güneri

https://doi.org/10.17776/csj.969445

Abstract

The objective of this study is to reveal the COVID19 characteristics of the countries by using time series clustering. Up to now, various studies have been conducted for similar objectives. But, it has been observed that these studies belong to early time of pandemic and are involved limited number of countries. To analyze the characteristic of COVID19 more, this study has considered 111 countries and time period between the 4th of April 2020 and the 1st of January 2021. Fuzzy K-Medoid (FKM) is preferred as clustering method due to its three abilities: i) FKM enables to determine the similarities and differences between the countries in more detail by utilizing the membership degrees, ii) In FKM, cluster centers are selected among from objects in the data set. Thus, it has the ability of detecting the countries which represent the behavior of all countries, iii) FKM is a robust method against to outliers. Thanks to this ability, FKM prevents that the countries exhibiting abnormal behavior negatively affect to the clustering results. At the results of the analyses, it is observed that 111 countries have three different behaviors in terms of confirmed cases and five different behaviors in terms of deaths.

Keywords

Fuzzy K-medois Cluster validity Time series clustering COVID19

References

  • [1] Imtyaz A, Haleem A, Javaid M, Analysing Governmental Response to The COVID19 Pandemic, Journal of Oral Biology and Craniofacial Research, 10 (2020) 504-513.
  • [2] Zarikas V, Poulopous SG, Gareiou Z, Zervas E, Clustering Analysis of the Countries COVID19 Data Sets, Data in Brief, (2020) 31.
  • [3] Mahmoudi MR, Baleanu D, Mansor Z, Tuan BA, Pho K, Fuzzy Clustering Method to Compare The Spread Rate of COVID19 in The High-risk Countries, Chaos, Solitions and Fractals, (2020) 140.
  • [4] Alvarez E, Brida JG, Limas E, Comparisions of COVID19 dynamics in the different countries of the World using time series clustering, (2020), medRxiv.
  • [5] Hutagalung J, Ginantra NLWSR, Bhawika GW, Parwita WGS, Wanto A, Panjaitan PD. COVID19 Cases and Deaths in Southeast Asia Clustering Using K-means Clustering, Annual Conference on Science and Technology Research, Journal of Physics: Conference Series, (2021) 1783.
  • [6] Virgantari F, Faridhan YE. K-means clustering of COVID19 cases in Indonesia’s provinces, Proceedings of the International Conference on Global Optimization and Its Applications, Jakarta, Indonesia, November 21-22, (2020).
  • [7] Rojas F, Valenzuela O, Rojas I,. Estimation of COVID19 dynamics in the different states of the United States using time series clustering, (2020), medRxiv.
  • [8] Azarafza M, Azarafza M, Akgün H, Clustering Method for Spread Pattern Analysis of Corona-virus (COVID19) Infection in Iran, Journal of Applied Science, Engineering, Technology, and Education, 3(1) (2021).
  • [9] Crnogorac V, Grbic M, Dukanovic M, Matic D, Clustering of European countries and territories based on cumulative relative number of COVID19 patients in 2020, 20th International Symposium INFOTEH, (2021).
  • [10] Sadeghi B, Cheung RCY, Hanbury M, using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020, BMJ Open, (2021).
  • [11] Putra PMA, Kadyanan GAGA, Implementation of K-Means clustering algorithm in determining classification of the spread of the COVID19 virus in Bali, Jurnal Elektronik Ilmu Komputer Udayanan, 10(1) (2021).
  • [12] Utomo W, The comparision of k-means and k-medoids algorithms for clustering the spread of the COVID19 outbreak in Indonesia, ILKOM Jurnal Ilmiah, 13(1) (2021).
  • [13] Abdullah D, Susilo S, Ahmar AS, Rusli R, Hidayat R, The application of K-Means clustering for province clustering in Indonesia of the risk of the COVID-19 pandemic based on COVID-19 data, Quality and Quantity, (2021).
  • [14] Everitt B, Landau S, Leese M, Cluster analysis, 4th ed. London: Arnold, (2001).
  • [15] Hair J, Black W, Babin B, Anderson R, Tatham R, Multivariate data analysis. 6th ed. Uppersaddle River, N.J.: Pearson Prentice Hall, (2006).
  • [16] Jain A, Murty M, Flynn P, Data Clustering: A Review, ACM Computing Surveys, 31(3) (1999) 264-323.
  • [17] De Carvalho FAT, De Melo FM, Lechevallier Y, A Multi-view Relational Fuzzy C-medoid Vectors Clustering Algorithm, Neurocomputing, 163 (2015) 115-123.
  • [18] Rani S, Sikka G, Recent Techniques of Clustering of Time Series Data: A Survey, International Journal of Computer Applications, 52 (2012).
  • [19] Bezdek JC, Pattern Recognition with Fuzzy Objective Function Algoritms. New York: Plenum Press , (1981).
  • [20]Gustafson DE, Kessel WC, Fuzzy Clustering with a Fuzzy Covariance Matrix. IEEE CDC San Diego, (1979) 761-766.
  • [21]Hathaway RJ, Bezdek JC, Switching regression models and fuzzy clustering, IEEE Transactions On Fuzzy Systems, 1(3) (1993) 195–204.
  • [22] Krishnapuram R, Joshi A, Yi L, A fuzzy relative of the k-medoids algorithm with application to web document snippet clustering, IEEE International Fuzzy Systems, Conference Proceedings, (1999).
  • [23]Labroche N, New incremental fuzzy C medoids clustering algorithms, Annual Conference of the North American Fuzzy Information Processing Society—NAFIPS; Toronto, ON, Canada, (2010).
  • [24]Rousseeuw PJ, Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics, 20 (1987) 53–65.
  • [25] Xie X, Beni G, A Validity Measure for Fuzzy Clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(8) (1991) 841-846.
  • [26]Bezdek JC, Numerical taxonomy with fuzzy sets, Journal of Mathematical Biology, 1(1) (1974) 57-71.
  • [27] Li CS, The Improved Partition Coefficient, Procedia Engineering, 24 (2011) 534-538.
  • [28]Bezdek, JC, Cluster validity with fuzzy sets. Journal of cybernetics, 3(3) (1974) 58-72.
  • [29]Dave RN, Validating fuzzy partitions obtained through c-shells clustering, Pattern Recognition Letters, 17(6) (1996) 613-623.
There are 29 citations in total.

Details

Primary Language English
Subjects Statistics
Journal Section Natural Sciences
Authors

Muhammet Oğuzhan Yalçın 0000-0003-4017-5588

Nevin Güler Dincer 0000-0003-0361-1803

Öznur İşçi Güneri 0000-0003-3677-7121

Publication Date March 30, 2022
Submission Date July 10, 2021
Acceptance Date March 5, 2022
Published in Issue Year 2022Volume: 43 Issue: 1

Cite

APA Yalçın, M. O., Güler Dincer, N., & İşçi Güneri, Ö. (2022). Investigating the COVID19 Characteristics of the Countries Based on Time Series Clustering. Cumhuriyet Science Journal, 43(1), 146-164. https://doi.org/10.17776/csj.969445
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