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Some Developments in Clustering Analysis on Stochastic Processes

Author

Listed:
  • Qidi Peng
  • Nan Rao

    (Institute of Mathematical Sciences, Claremont Graduate University, USA)

  • Ran Zhao

    (Drucker School of Management and Institute of Mathematical Sciences, Claremont Graduate University, USA)

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.

Suggested Citation

  • Qidi Peng & Nan Rao & Ran Zhao, 2019. "Some Developments in Clustering Analysis on Stochastic Processes," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(3), pages 72-77, April.
    • Handle: RePEc:adp:jbboaj:v:9:y:2019:i:3:p:72-77
    DOI: 10.19080/BBOAJ.2019.09.555764
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    References listed on IDEAS

    as
    1. Ian C McDowell & Dinesh Manandhar & Christopher M Vockley & Amy K Schmid & Timothy E Reddy & Barbara E Engelhardt, 2018. "Clustering gene expression time series data using an infinite Gaussian process mixture model," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-27, January.
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    Cited by:

    1. Lauri Varmann & Helena Mouriño, 2024. "Clustering Empirical Bootstrap Distribution Functions Parametrized by Galton–Watson Branching Processes," Mathematics, MDPI, vol. 12(15), pages 1-25, August.

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