Some Developments in Clustering Analysis on Stochastic Processes
This work addresses clustering challenges in stochastic process analysis, but it appears incremental as it reviews and synthesizes existing developments rather than introducing new methods.
The paper reviews clustering methods for stochastic processes and concludes that asymptotically consistent clustering algorithms can be achieved when processes are ergodic and the dissimilarity measure satisfies the triangle inequality, with examples provided for distribution ergodic, covariance ergodic, and locally asymptotically self-similar processes.
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.