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.
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Bishehsari, F. et al. (2007). PCA Based Feature Selection Applied to the Analysis of the International Variation in Diet. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_70
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DOI: https://doi.org/10.1007/978-3-540-73400-0_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73399-7
Online ISBN: 978-3-540-73400-0
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