Smart Visualization of Mixed Data

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This is based on the combination of Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, Verdolini 2020) clustering. The resulting groups are visualized via MDS maps characterized through an analysis several graphical outputs. methodology illustrated real dataset related European COVID-19 numerical health data, as well policy restriction measurements 2020–2021 pandemic across EU Member States. results show similarities among countries in terms incidence management emergency waves disease. With proposed methodology, smart tools for analyzing data provided.