Post-processing the Class Panel Graphs: towards understandable patterns from data

Abstract

A profiling methodology is introduced for automatic interpretation of clusters in this work. This methodology contributes to the characterization of the resulting classes from a clustering process. This work aims to find a concordance between the proposed methodology and the experts’ description of these classes. In this work the resulting classes from a clustering of a general population sample based on their diet and physical activity habits are interpreted and compared with the experts’ description of these classes by using the Class Panel Graphs. In this work, we import techniques from the multivariate analysis into the cluster interpretation process.