PCA Based Feature Selection Applied to the Analysis of the International Variation in Diet

Authors:
Faraz Bishehsari;Mahboobeh Mahdavinia;Reza Malekzadeh;Renato Mariani-Costantini;Gennaro Miele;Francesco Napolitano;Giancarlo Raiconi;Roberto Tagliaferri;Fabio Verginelli
Affiliations:
DDRC, Tehran University of Medical Sciences, Tehran, Iran;DDRC, Tehran University of Medical Sciences, Tehran, Iran;DDRC, Tehran University of Medical Sciences, Tehran, Iran;DON, University G. d'Annunzio, and Ce.S.I., G. D'Annunzio Foundation, Chieti,;DSF, University of Naples, I-80136, via Cintia 6, Napoli, Italy,;DMI, University of Salerno, I-84084, via Ponte don Melillo, Fisciano (SA), Italy,;DMI, University of Salerno, I-84084, via Ponte don Melillo, Fisciano (SA), Italy,;DMI, University of Salerno, I-84084, via Ponte don Melillo, Fisciano (SA), Italy,;DON, University G. d'Annunzio, and Ce.S.I., G. D'Annunzio Foundation, Chieti,
Venue:
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Year:
2007

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Abstract

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.