We present an approach to the construction of clusters of life course trajectories and use it to obtain ideal-types of trajectories that can be interpreted and analyzed meaningfully. We represent life courses as sequences on a monthly time scale and apply optimal matching analysis to compute dissimilarity between individuals. We introduce a new divisive algoorithm which has features that are in common with both Ward's agglomerative algorithm and classification and regression trees.

Clustering Work and Family Trajectories by using a Divisive Algorithm

PICCARRETA, RAFFAELLA;BILLARI, FRANCESCO CANDELORO
2007

Abstract

We present an approach to the construction of clusters of life course trajectories and use it to obtain ideal-types of trajectories that can be interpreted and analyzed meaningfully. We represent life courses as sequences on a monthly time scale and apply optimal matching analysis to compute dissimilarity between individuals. We introduce a new divisive algoorithm which has features that are in common with both Ward's agglomerative algorithm and classification and regression trees.
2007
Piccarreta, Raffaella; Billari, FRANCESCO CANDELORO
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/51547
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 45
  • ???jsp.display-item.citation.isi??? 40
social impact