Material deprivation is a complex concept referring to the inability of families to meet certain needs. Some indicators of material deprivation, included in the EU portfolio, are collected by EU-SILC survey on households and individuals through categorical variables. In this study, the large dataset provided by EU-SILC survey collected in Italy in 2017, on a sample of 22,226 households is analysed. The main goal is to identify clusters of Italian households to take into account the multiple aspects of material deprivation conditions, including environmental ones. To that end, a multi-objective genetic algorithm as a clustering technique for categorical data is proposed. The results are compared with those obtained by applying a K-means algorithm to latent variables scores.
Italian Households’ Material Deprivation: Multi-Objective Genetic Algorithm approach for categorical variables / Bocci, L.; Mingo, I.. - (2020), pp. 1501-1506. (Intervento presentato al convegno SIS 2020 tenutosi a Pisa).
Italian Households’ Material Deprivation: Multi-Objective Genetic Algorithm approach for categorical variables
Bocci L.;Mingo I.
2020
Abstract
Material deprivation is a complex concept referring to the inability of families to meet certain needs. Some indicators of material deprivation, included in the EU portfolio, are collected by EU-SILC survey on households and individuals through categorical variables. In this study, the large dataset provided by EU-SILC survey collected in Italy in 2017, on a sample of 22,226 households is analysed. The main goal is to identify clusters of Italian households to take into account the multiple aspects of material deprivation conditions, including environmental ones. To that end, a multi-objective genetic algorithm as a clustering technique for categorical data is proposed. The results are compared with those obtained by applying a K-means algorithm to latent variables scores.File | Dimensione | Formato | |
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