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

https://doi.org/10.1016/j.jairtraman.2022.102194Get rights and content
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Highlights

  • COVID-19 has caused an unprecedented impact in the air transport sector.

  • An unsupervised clustering method is proposed for dynamic market visualization.

  • The Data Mining algorithms employed are Self-Organizing Maps (SOMs) and K-means.

  • The model is applied to data from 18 diverse US passenger airlines from 1991 to 2020.

  • Market segmentation, M&A events or the COVID-19 effect can be assessed.

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

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