Scrucca, Luca

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Scrucca, Luca
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Scrucca, Luca

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  • Publication
    mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
    (R Foundation for Statistical Computing, 2016-08-01)
    Scrucca, Luca 
    ;
    Fop, Michael 
    ;
    Murphy, Thomas Brendan 
    ;
    Raftery, Adrian E. 
    Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.
      1181
  • Publication
    Model-based clustering with sparse covariance matrices
    (Springer, 2019)
    Fop, Michael 
    ;
    Murphy, Thomas Brendan 
    ;
    Scrucca, Luca 
    Finite Gaussian mixture models are widely used for model-based clustering of continuous data. Nevertheless, since the number of model parameters scales quadratically with the number of variables, these models can be easily over-parameterized. For this reason, parsimonious models have been developed via covariance matrix decompositions or assuming local independence. However, these remedies do not allow for direct estimation of sparse covariance matrices nor do they take into account that the structure of association among the variables can vary from one cluster to the other. To this end, we introduce mixtures of Gaussian covariance graph models for model-based clustering with sparse covariance matrices. A penalized likelihood approach is employed for estimation and a general penalty term on the graph configurations can be used to induce different levels of sparsity and incorporate prior knowledge. Model estimation is carried out using a structural-EM algorithm for parameters and graph structure estimation, where two alternative strategies based on a genetic algorithm and an efficient stepwise search are proposed for inference. With this approach, sparse component covariance matrices are directly obtained. The framework results in a parsimonious model-based clustering of the data via a flexible model for the within-group joint distribution of the variables. Extensive simulated data experiments and application to illustrative datasets show that the method attains good classification performance and model quality. The general methodology for model-based clustering with sparse covariance matrices is implemented in the R package mixggm, available on CRAN.
      402Scopus© Citations 16