Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Darío Pérez‐Campuzano · Luís Rubio Andrada · Patricio Morcillo Ortega · Antonio López-Lázaro

8 citations0/0 files storedJournal of Air Transport Management

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Aviation Industry Analysis and Trends Data Visualization and Analytics Data Mining and Machine Learning Applications

Abstract

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

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2021COVID-19 pandemic and prospects for recovery of the global aviation industry 2020An early assessment of the impact of COVID-19 on air transport: Just another crisis or the end of aviation as we know it?2020Government support to airlines in the aftermath of the COVID-19 pandemic 2020The impacts of COVID-19 on the global airline industry: An event study approach 2017An integrated MCDM model for improving airline operational and financial performance 2008Towards a means of consistently comparing airline business models with an application to the ‘low cost’ airline sector 2012The airline business model spectrum 2020What can we learn from the JATM literature for the future of aviation post Covid-19? - A bibliometric and visualization analysis

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