Abstract The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view.


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    Titel :

    Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data


    Beteiligte:
    Suzdaleva, Evženie (Autor:in) / Nagy, Ivan (Autor:in)


    Erscheinungsdatum :

    18.04.2019


    Format / Umfang :

    20 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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