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

Pérez-Campuzano, Darío, Luis Rubio Andrada, Patricio Morcillo Ortega and López-Lázaro, Antonio

Journal of Air Transport Management, 2022, vol. 101, issue C

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.

Keywords: Airlines; COVID-19; Data mining (DM); Unsupervised learning; Self-organizing map (SOM); K-means (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaitra:v:101:y:2022:i:c:s0969699722000151

DOI: 10.1016/j.jairtraman.2022.102194

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