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    Gaussian parsimonious clustering models with covariates and a noise component


    Murphy, Keefe and Murphy, Thomas Brendan (2020) Gaussian parsimonious clustering models with covariates and a noise component. Advances in Data Analysis and Classification, 14 (2). pp. 293-325. ISSN 1862-5347

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    Abstract

    We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These models allow different subsets of covariates to influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian parsimonious clustering models and incorporating parsimonious covariance structures into all special cases of the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to both univariate and multivariate data sets. Novel extensions to include a uniform noise component for capturing outliers and to address initialisation of the EM algorithm, model selection, and the visualisation of results are also proposed.

    Item Type: Article
    Additional Information: This is the preprint version of the published article, which is available at: Murphy, K., Murphy, T.B. Gaussian parsimonious clustering models with covariates and a noise component. Adv Data Anal Classif 14, 293–325 (2020). https://doi.org/10.1007/s11634-019-00373-8
    Keywords: Model-based clustering; Mixtures of experts; EM algorithm; Parsimony; Multivariate response; Covariates; Noise component;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Item ID: 15563
    Identification Number: https://doi.org/10.1007/s11634-019-00373-8
    Depositing User: Keefe Murphy
    Date Deposited: 23 Feb 2022 15:05
    Journal or Publication Title: Advances in Data Analysis and Classification
    Publisher: Springer
    Refereed: No
    URI:

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