Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
Entidad
UAM. Departamento de Economía AplicadaEditor
ElsevierFecha de edición
2022-02-28Cita
10.1016/j.jairtraman.2022.102194
Journal of Air Transport Management 101 (2022): 102194
ISSN
0969-6997 (print)DOI
10.1016/j.jairtraman.2022.102194Financiado por
This work was supported by LLM Aviation and EuroairlinesVersión del editor
https://doi.org/10.1016/j.jairtraman.2022.102194Materias
Airlines; COVID-19; Data mining (DM); Unsupervised learning; Self-organizing map (SOM); K-means; EconomíaDerechos
© 2022 The AuthorsEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Resumen
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 Kmeans) 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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Google Scholar:Pérez Campuzano, Darío
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Rubio Andrada, Luis
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Morcillo Ortega, Patricio
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López Lázaro, Antonio
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