  This paper proposes a novel similarity measure for clustering sequential
data. We first construct a common state-space by training a single
probabilistic model with all the sequences in order to get a unified
representation for the dataset. Then, distances are obtained attending to the
transition matrices induced by each sequence in that state-space. This approach
solves some of the usual overfitting and scalability issues of the existing
semi-parametric techniques, that rely on training a model for each sequence.
Empirical studies on both synthetic and real-world datasets illustrate the
advantages of the proposed similarity measure for clustering sequences.
