  Sequence analysis is an increasingly popular approach for analysing life
courses represented by ordered collections of activities experienced by
subjects over time. Here, we analyse a survey data set containing information
on the career trajectories of a cohort of Northern Irish youths tracked between
the ages of 16 and 22. We propose a novel, model-based clustering approach
suited to the analysis of such data from a holistic perspective, with the aims
of estimating the number of typical career trajectories, identifying the
relevant features of these patterns, and assessing the extent to which such
patterns are shaped by background characteristics.
  Several criteria exist for measuring pairwise dissimilarities among
categorical sequences. Typically, dissimilarity matrices are employed as input
to heuristic clustering algorithms. The family of methods we develop instead
clusters sequences directly using mixtures of exponential-distance models.
Basing the models on weighted variants of the Hamming distance metric permits
closed-form expressions for parameter estimation. Simultaneously allowing the
component membership probabilities to depend on fixed covariates and
accommodating sampling weights in the clustering process yields new insights on
the Northern Irish data. In particular, we find that school examination
performance is the single most important predictor of cluster membership.
