Title: Exploring outliers in global economic dataset having the impact of COVID-19 pandemic

Authors: Anindita Desarkar; Ajanta Das; Chitrita Chaudhuri

Addresses: Computer Science and Engineering, Jadavpur University, Kolkata 700032, India ' Amity Institute of Information Technology, Amity University, Kolkata, India ' Computer Science and Engineering, Jadavpur University, Kolkata 700032, India

Abstract: Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently.

Keywords: economic outlier; machine learning; gross domestic product; GDP; per capita; human development index; HDI; COVID-19 pandemic; total death percentage; total infection percentage; unemployment rate.

DOI: 10.1504/IJBIDM.2023.129877

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.3, pp.287 - 309

Received: 11 Mar 2021
Accepted: 17 Sep 2021
Published online: 03 Apr 2023 *

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