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...

Descripción completa

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
_version_ 1784852216591417344
author Pérez-Campuzano, Darío
Rubio Andrada, Luis
Morcillo Ortega, Patricio
López-Lázaro, Antonio
author_facet Pérez-Campuzano, Darío
Rubio Andrada, Luis
Morcillo Ortega, Patricio
López-Lázaro, Antonio
author_sort Pérez-Campuzano, Darío
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9759375
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher The Authors. Published by Elsevier Ltd.
record_format MEDLINE/PubMed
spelling pubmed-97593752022-12-19 Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance Pérez-Campuzano, Darío Rubio Andrada, Luis Morcillo Ortega, Patricio López-Lázaro, Antonio J Air Transp Manag Article 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. The Authors. Published by Elsevier Ltd. 2022-06 2022-02-28 /pmc/articles/PMC9759375/ /pubmed/36568914 http://dx.doi.org/10.1016/j.jairtraman.2022.102194 Text en © 2022 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Pérez-Campuzano, Darío
Rubio Andrada, Luis
Morcillo Ortega, Patricio
López-Lázaro, Antonio
Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title_full Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title_fullStr Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title_full_unstemmed Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title_short Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance
title_sort visualizing the historical covid-19 shock in the us airline industry: a data mining approach for dynamic market surveillance
topic Article
url 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
work_keys_str_mv AT perezcampuzanodario visualizingthehistoricalcovid19shockintheusairlineindustryadataminingapproachfordynamicmarketsurveillance
AT rubioandradaluis visualizingthehistoricalcovid19shockintheusairlineindustryadataminingapproachfordynamicmarketsurveillance
AT morcilloortegapatricio visualizingthehistoricalcovid19shockintheusairlineindustryadataminingapproachfordynamicmarketsurveillance
AT lopezlazaroantonio visualizingthehistoricalcovid19shockintheusairlineindustryadataminingapproachfordynamicmarketsurveillance

Ejemplares similares