Exploratory techniques to analyse Ecuador's tourism industry.

This article focuses on applying Soft Computing techniques to analyze the operation of tourism companies in Ecuador, aiming to generate information that supports the reactivation of the tourism industry affected by the COVID-19 pandemic. Using data from 2015, 2019, and 2020, the study employs dimensionality reduction methods—Principal Component Analysis (PCA), Isometric Mapping (ISOMAP), and Locally Linear Embedding (LLE)—and clustering algorithms including k-means, k-medoids, and hierarchical clustering, with evaluation metrics such as the Elbow Method, Silhouette Coefficient, Davies–Bouldin Index, and Dunn Index to identify optimal cluster groupings. Results indicate strong correlations among operational variables and reveal a decline in tourism company activity in 2019 and 2020, likely linked to social protests and the pandemic. The findings suggest that these computational techniques can inform tailored strategies and public policies, such as targeted credit lines and promotional campaigns, to aid the sustainable recovery of Ecuador’s tourism sector.