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In Journal of air transport management

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.

Pérez-Campuzano Darío, Rubio Andrada Luis, Morcillo Ortega Patricio, López-Lázaro Antonio


Airlines, COVID-19, Data mining (DM), K-means, Self-organizing map (SOM), Unsupervised learning