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

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 fr...

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Detalles Bibliográficos
Autores principales: Pérez-Campuzano, Darío, Rubio Andrada, Luis, Morcillo Ortega, Patricio, López-Lázaro, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Ltd. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759375/
https://www.ncbi.nlm.nih.gov/pubmed/36568914
http://dx.doi.org/10.1016/j.jairtraman.2022.102194
Descripción
Sumario: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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