Curves clustering with approximation of the density of functional random variables

نویسندگان

  • Julien Jacques
  • Cristian Preda
چکیده

Model-based clustering for functional data is considered. An alternative to model-based clustering using the functional principal components is proposed by approximating the density of functional random variables. An EM-like algorithm is used for parameter estimation and the maximum a posteriori rule provides the clusters. Real data applications illustrate the interest of the proposed methodology.

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تاریخ انتشار 2012