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. •COVID-19 has caused an unprecedented impact in the air transport sector.•An unsupervised clustering method is proposed for dynamic market visualization.•The Data Mining algorithms employed are Self-Organizing Maps (SOMs) and K-means.•The model is applied to data from 18 diverse US passenger airlines from 1991 to 2020.•Market segmentation, M&A events or the COVID-19 effect can be assessed.