Comparison of K-Means, K-Medoids and K-Means++ algorithms based on the Calinski-Harabasz index for COVID-19 epidemic in China

Zhengcao Hu

doi:10.54254/2755-2721/49/20241046

Article Information

Received: 23 February 2024

Published: 22 March 2024

DOI: https://doi.org/10.54254/2755-2721/49/20241046

Article Type: Research Article

Cite this article

Hu,Z. (2024). Comparison of K-Means, K-Medoids and K-Means++ algorithms based on the Calinski-Harabasz index for COVID-19 epidemic in China. Applied and Computational Engineering,49,11-20.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title: ACE Vol.49

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

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[1]M. A. Shereen, S. Khan, A. Kazmi, NBashir, and R. Siddique, COVID- 19 infection: Origin, transmission, and characteristics of human coronaviruses, Journal of Advanced Research 24 (2020) 91–98.

[2]N.Dwitri dkk, Penerapan Algoritma K-Means dalam Menentukan Tingkat Penyebaran Pandemi Covid- 19 di Indonesia, Jurnal Teknologi Informasi, Vol. 4, No. 1, Juni 2020.

[3]R.A. Indraputra , R. Fitriana, K-Means Clustering Data COVID- 19, Jurnal Teknik Industri, Volume 10 No.3.Desember 2020.

[4]Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Hierarchical Clustering Analysis of COVID-19 Transmission in Wuhan, China. Journal of Medical Virology, 92(9), 1887-1895.

[5]Sun, Y., Li, Y., Bao, Y., Meng, S., Sun, Y., Schumann-Bischoff, J.,... & Luan, H. (2020). Identifying Links Between SARS-CoV-2 Transmission and Clustered Environments. Journal of Travel Medicine, 27(5), taaa099.

[6]Liu, L., Wei, Q., Alvarez, X., Wang, H., Du, Y., Zhu, H.,... & Chen, Z. (2020). Epithelial Cells lining Salivary Gland Ducts are Early Target Cells of Severe Acute Respiratory Syndrome Coronavirus Infection in The Upper Respiratory Tracts of Rhesus Macaques. Journal of Virology, 84(15), 765-771.

[7]Zhang, J., Zhou, L., Yang, Y., Peng, W., Wang, W., Chen, X.,... & Liu, Z. (2020). Therapeutic and Triaging Strategies for 2019 Novel Coronavirus Disease in Fever Clinics. The Lancet Respiratory Medicine, 8(3), e11-e12.

[8]NetEase. (n.d.). Virus Report. Retrieved from https://wp.m.163.com/163/page/news/virus_report/index.html.

[9]China Centers for Disease Control and Prevention. (n.d.). Health and Wellness. Retrieved from https://m.chinacdc.cn/xwzx/zxyw/.

[10]Li Cuixia, Yu Jian. A study on classification of fuzzy clustering algorithm [J]. Journal of Beijing Jiaotong University: Natural Science Edition, 2005, 29(2): 17-21.

[11]Witten, I. H., & Frank, E. (2005). An Introduction to Data Mining.

[12]Mitchell, T. M. (1997). Machine Learning. McGraw Hill.

[13]Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.

[14]Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of medoids. Statistical Data Analysis Based on the L1-Norm and Related Methods, 405-416.

[15]Park, H. S., Jun, C. H., & Park, H. H. (2009). A partitioning around medoids-based clustering algorithm for large-scale data sets. Data Mining and Knowledge Discovery, 18(3), 359-390.

[16]Arthur,D.,&Vassilvitskii,S. (2007). K-Means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms.

[17]Bahmani,B.,Moseley, B.,Vattani, A., Kumar, R., & Vassilvitskii, S. (2012). Scalable K-Means++. Proceedings of the VLDB Endowment, 5(7), 622-633.

[18]Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics, 3(1), 1-27.

[19]Nguyen,T.X.,Vo, B.,& Cao, H. (2017). An efficient clustering algorithm for image segmentation. International Journal of Computer Vision, 123(2), 312-328.