  The problem of change-point estimation is considered under a general
framework where the data are generated by unknown stationary ergodic process
distributions. In this context, the consistent estimation of the number of
change-points is provably impossible. However, it is shown that a consistent
clustering method may be used to estimate the number of change points, under
the additional constraint that the correct number of process distributions that
generate the data is provided. This additional parameter has a natural
interpretation in many real-world applications. An algorithm is proposed that
estimates the number of change-points and locates the changes. The proposed
algorithm is shown to be asymptotically consistent; its empirical evaluations
are provided.
