From sequences to variables - Rethinking the relationship between sequences and outcomes

Abstract Sequence analysis (SA) has gained increasing interest in social sciences for the holistic analysis of life course and other longitudinal data. The usual approach is to construct sequences, calculate dissimilarities, group similar sequences with cluster analysis, and use cluster membership as a dependent or independent variable in a linear or nonlinear regression model. This approach may be problematic as the cluster memberships are assumed to be fixed known characteristics of the subjects in subsequent analysis.

Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data

Abstract Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group.

Analysing Complex Life Sequence Data with Hidden Markov Modelling

Abstract When analysing complex sequence data with multiple channels (dimensions) and long observation sequences, describing and visualizing the data can be a challenge. Hidden Markov models (HMMs) and their mixtures (MHMMs) offer a probabilistic model-based framework where the information in such data can be compressed into hidden states (general life stages) and clusters (general patterns in life courses). We studied two different approaches to analysing clustered life sequence data with sequence analysis (SA) and hidden Markov modelling.

Minimum description length based hidden Markov model clustering for life sequence analysis

Abstract In this article, a model-based method for clustering life sequences is suggested. In the social sciences, model-free clustering methods are often used in order to find typical life sequences. The suggested method, which is based on hidden Markov models, provides principled probabilistic ranking of candidate clusterings for choosing the best solution. After presenting the principle of the method and algorithm, the method is tested with real life data, where it finds eight descriptive clusters with clear probabilistic structures.