Abstract
The multidimensional nature of socio-economic hardship requires a multidimensional research approach, oriented toward advanced solutions, to be able to capture the changing dimensions of the problem at hand. One of such approaches consists of abandoning traditional dichotomous logic in favour of a semantically richer fuzzy classification, in which each unit belongs and, at the same time, does not belong to a given category. Cluster analysis allows us to identify the profiles of families who meet certain descriptive characteristics not defined a priori. The approach used in this work to synthesise and measure hardship conditions is based on a clustering procedure known as fuzzy clustering by local approximation of membership (FLAME), and based on defining the neighbourhood of each object and identifying cluster-supporting objects. This clustering method not only allows for each instance of a dataset to belong to a unique main cluster, but also that each instance can be shared by two or more clusters on the ground of suitably defined 'fuzzy profiles'.
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