We introduce a new unsupervised learning problem: clustering widesense 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.Keywords: cluster analysis¨wide-sense stationary ergodic processesc ovariance-based dissimilarity measure¨self-similar processes MCS (2010): 62-07¨60G10¨62M10

No abstract

No abstract

No abstract

No abstract

No abstract

No abstract

No abstract