Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

Asif Afzal; Zahid Ansari; Saad Alshahrani; Arun K Raj; Mohamed Saheer Kuruniyan; C Ahamed Saleel; Kottakkaran Sooppy Nisar

doi:10.1016/j.rinp.2021.104639

Clustering of COVID-19 data for knowledge discovery using c-means and fuzzy c-means

Results Phys. 2021 Oct:29:104639. doi: 10.1016/j.rinp.2021.104639. Epub 2021 Aug 21.

Authors

Asif Afzal¹, Zahid Ansari², Saad Alshahrani³, Arun K Raj⁴, Mohamed Saheer Kuruniyan⁵, C Ahamed Saleel³, Kottakkaran Sooppy Nisar⁶

Affiliations

¹ Department of Mechanical Engineering, P. A. College of Engineering (Affiliated to Visvesvaraya Technological University, Belagavi), Mangaluru, India.
² Electrical Engineering Section, University Polytechnic, Aligarh Muslim University, Aligarh, India.
³ Department of Mechanical Engineering, College of Engineering, King Khalid University, PO Box 394, Abha 61421, Kingdom of Saudi Arabia.
⁴ Department of Mechanical Engineering, Indian Institute of Technology Bombay, Maharashtra 400076, India.
⁵ Department of Dental Technology, College of Applied Medical Science, King Khalid University, Asir-Abha, Kingdom of Saudi Arabia.
⁶ Department of Mathematics, College of Arts and Sciences, Wadi Aldawaser, Prince Sattam bin Abdulaziz University, Saudi Arabia.

Abstract

In this work, the partitioning clustering of COVID-19 data using c-Means (cM) and Fuzy c-Means (Fc-M) algorithms is carried out. Based on the data available from January 2020 with respect to location, i.e., longitude and latitude of the globe, the confirmed daily cases, recoveries, and deaths are clustered. In the analysis, the maximum cluster size is treated as a variable and is varied from 5 to 50 in both algorithms to find out an optimum number. The performance and validity indices of the clusters formed are analyzed to assess the quality of clusters. The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama-Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. The analysis results pointed out that five clusters were identified as a major centroid where the pandemic looks concentrated. Additionally, the observations revealed that mainly the pandemic is distributed easily at any global location, and there are several centroids of COVID-19, which primarily act as epicentres. However, the three main COVID-19 clusters identified are 1) cases with value <50,000, 2) cases with a value between 0.1 million to 2 million, and 3) cases above 2 million. These centroids are located in the US, Brazil, and India, where the rest of the small clusters of the pandemic look oriented. Furthermore, the Fc-M technique seems to provide a much better cluster than the c-M algorithm.

Keywords: COVID-19; Fuzzy c-means; Location; Validity index; c-Means.