The Lifestyles of Families through Fuzzy C-Means Clustering

Montrone, Silvestro; Perchinunno, Paola; L‘Abbate, Samuela; Zitolo, Maria Rosaria

doi:10.1007/978-3-319-09150-1_10

Cited by 4 publications

(2 citation statements)

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“…-Create "fuzzy profiles" highlighting the specific peculiarities of small groups not strictly belonging to a defined profile but to a mix of different profiles, having different levels membership to the Main clusters [28,29,30,31,32,33,34].…”

Section: Discussionmentioning

confidence: 99%

Fuzzy cluster and validity indices in a socio-economic context

Montrone

Perchinunno

L‘Abbate

2017

IJBIDM

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The numerous concepts of socio-economic hardship are, furthermore, attributable to a traditional distinction between absolute and relative conditions of hardship. The options of scientific research were therefore oriented towards the establishment of a multi-dimensional approach, sometimes abandoning dichotomous logic in order to arrive at fuzzy classifications in which each unit belongs and, at the same time, does not belong, to a category. The cluster analysis allows to identify the profiles families who meet certain descriptive characteristics, not defined a priori. The approach used in this work to synthesize and measure the conditions of the hardship of a population is based on a clustering procedure which is known as Fuzzy clustering by Local Approximation of Membership (FLAME) worked by defining the neighborhood of each object and identifying cluster supporting objects. This clustering method allows a set of data to belong not only to a main cluster but also to two or more clusters with “fuzzy profiles”

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Section: Discussionmentioning

confidence: 99%

Fuzzy cluster and validity indices in a socio-economic context

Montrone

Perchinunno

L‘Abbate

2017

IJBIDM

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Fuzzy cluster and validity indices in a socio-economic context

Montrone

Perchinunno

et al. 2017

IJBIDM

Self Cite

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The multidimensional nature of socio-economic hardship requires a multidimensional research approaches, oriented toward advanced solutions, able to capture the changing dimensions of the problem at hand. One of such approaches consists in abandoning traditional dichotomous logic in favor 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 to identify the profiles families who meet certain descriptive characteristics not defined a priori. The approach used in this work to synthesize 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 neighborhood of each object and identifying cluster supporting objects. This clustering method not only allows for each instance of a data set 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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