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Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market
Darío Pérez-Campuzano1,2, Luis Rubio Andrada1, Patricio Morcillo Ortega1
1Universidad Autónoma de Madrid (UAM), Facultad de Ciencias Económicas y Empresariales, Calle Francisco Tomás y Valiente N5, 29049, Madrid, Spain.
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.
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