Some Developments in Clustering Analysis on Stochastic Processes
Machine Learning
2019-08-07 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1908.01794,
title = {Some Developments in Clustering Analysis on Stochastic Processes},
author = {Qidi Peng and Nan Rao and Ran Zhao},
journal= {arXiv preprint arXiv:1908.01794},
year = {2019}
}