Work

Visualizing the historical COVID-19 shock in the US airline industry: A Data Mining approach for dynamic market surveillance

Year: 2022

Type: article

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.

Source: Journal of Air Transport Management

DOI: 10.1016/j.jairtraman.2022.102194

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

Institutions: Universidad Autónoma de Madrid, Universidad Politécnica de Madrid

Language: English

Topic: Aviation Industry Analysis and Trends

Subfield: General Economics, Econometrics and Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,660

Funder: Universidad Autónoma de Madrid