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.
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