PCA based feature selection applied to the analysis of the international variation in diet

In this work we describe a clustering and feature selection technique applied to the analysis of international dietary profiles. An asymmetric entropy-based measure for assessing the similarity between two clusterizations, also taking into account subclustering relationships, is at the core of the technique, together with PCA. Then, a feature analysis of the dataset with respect to its hierarchical clusterization is performed. This way, most significant features of the dataset are found and a deep understanding of the data distribution is made possible. © Springer-Verlag Berlin Heidelberg 2007.