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

被引：0
作者：
Desarkar A. [1 ]
Das A. [2 ]
Chaudhuri C. [1 ]
机构：
[1] Computer Science and Engineering, Jadavpur University, Kolkata
[2] Amity Institute of Information Technology, Amity University, Kolkata
来源：
International Journal of Business Intelligence and Data Mining | 2023年 / 22卷 / 03期
关键词：
COVID-19; pandemic; economic outlier; GDP; gross domestic product; HDI; human development index; machine learning; per capita; total death percentage; total infection percentage; unemployment rate;
D O I：
10.1504/IJBIDM.2023.129877
中图分类号：
学科分类号：
摘要：
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. Copyright © 2023 Inderscience Enterprises Ltd.
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页码：287 / 309
页数：22
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