  This paper introduces a novel model-based clustering approach for clustering
time series which present changes in regime. It consists of a mixture of
polynomial regressions governed by hidden Markov chains. The underlying hidden
process for each cluster activates successively several polynomial regimes
during time. The parameter estimation is performed by the maximum likelihood
method through a dedicated Expectation-Maximization (EM) algorithm. The
proposed approach is evaluated using simulated time series and real-world time
series issued from a railway diagnosis application. Comparisons with existing
approaches for time series clustering, including the stand EM for Gaussian
mixtures, $K$-means clustering, the standard mixture of regression models and
mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed
approach.
