Data Science for Decision-Making in Public Health: A robust Clustering Approach for Large-Scale Weighted and Mixed-Type Socio-Economic Data
Published in Submitted for publication, 2027
A novel distance-based unsupervised statistical learning framework for large-scale weighted mixed-type data to identify multidimensional health and well-being profiles among older Europeans.
Recommended citation: Albarrán, I., Grané, A., & Scielzo-Ortiz, F. (2026). "Data Science for Decision-Making in Public Health: A robust Clustering Approach for Large-Scale Weighted and Mixed-Type Socio-Economic Data." (Submitted for publication at Socio-Economic Planning Sciences).
