  We introduce a new unsupervised learning problem: clustering wide-sense
stationary ergodic stochastic processes. A covariance-based dissimilarity
measure together with asymptotically consistent algorithms is designed for
clustering offline and online datasets, respectively. We also suggest a formal
criterion on the efficiency of dissimilarity measures, and discuss of some
approach to improve the efficiency of our clustering algorithms, when they are
applied to cluster particular type of processes, such as self-similar processes
with wide-sense stationary ergodic increments. Clustering synthetic data and
real-world data are provided as examples of applications.
