Some Developments in Clustering Analysis on Stochastic 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.

• Publication year

  2019

• Venue

  arXiv.org

• Publication date

  2019-08-05

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1908.01794
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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