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 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.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/31005945
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Supplemental Notes:
- © 2022 Darío Pérez-Campuzano et al. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
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Authors:
- Pérez-Campuzano, Darío
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0000-0002-3651-1262
- Rubio Andrada, Luis
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0000-0001-6870-1813
- Morcillo Ortega, Patricio
- López-Lázaro, Antonio
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: 102194
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Serial:
- Journal of Air Transport Management
- Volume: 101
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0969-6997
- Serial URL: http://www.sciencedirect.com/science/journal/09696997
Subject/Index Terms
- TRT Terms: Airlines; COVID-19; Data mining; Decision making; Economic impacts
- Subject Areas: Administration and Management; Aviation; Economics;
Filing Info
- Accession Number: 01838748
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 16 2022 10:22AM