Post-Processing the Class Panel Graphs: Towards Understandable Patterns from Data
Sevilla-Villanueva Beatriz, Karina Gibert, S agrave nchez-Marr egrave Miquel · Frontiers in artificial intelligence and applications · 2013
A profiling methodology is introduced for automatic interpretation of clusters in this paper. This methodology contributes to the characterization of the resulting classes from a clustering process. Our research 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. As a novelty, we import techniques from the multivariate analysis into the cluster interpretation process.