  We review some developments on clustering stochastic processes and come with
the conclusion that asymptotically consistent clustering algorithms can be
obtained when the processes are ergodic and the dissimilarity measure satisfies
the triangle inequality. Examples are provided when the processes are
distribution ergodic, covariance ergodic and locally asymptotically
self-similar, respectively.
