MULTIMIX

Mixture model clustering using the MULTIMIX program L. Hunt [Clustering using finite mixture models. Ph.D. thesis, Univ. Waikato (1996)] implemented the finite mixture model approach to clustering in a program called MULTIMIX. The program is designed to cluster multivariate data that have categorical and continuous variables and that possibly contain missing values. This paper describes the approach taken to design MULTIMIX and how some of the statistical problems were dealt with. As an example, the program is used to cluster a large medical dataset.


References in zbMATH (referenced in 25 articles , 1 standard article )

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  1. Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
  2. Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung: Mixtures of regression models with incomplete and noisy data (2018)
  3. Azzalini, Adelchi; Menardi, Giovanna: Density-based clustering with non-continuous data (2016)
  4. Punzo, Antonio; Ingrassia, Salvatore: Clustering bivariate mixed-type data via the cluster-weighted model (2016)
  5. Marbac, Matthieu; Biernacki, Christophe; Vandewalle, Vincent: Model-based clustering for conditionally correlated categorical data (2015)
  6. McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.: Clustering south african households based on their asset status using latent variable models (2014)
  7. Chen, Tao; Zhang, Nevin L.; Liu, Tengfei; Poon, Kin Man; Wang, Yi: Model-based multidimensional clustering of categorical data (2012)
  8. Morlini, Isabella: A latent variables approach for clustering mixed binary and continuous variables within a Gaussian mixture model (2012)
  9. Giordan, Marco; Diana, Giancarlo: A clustering method for categorical ordinal data (2011)
  10. Genolini, Christophe; Falissard, Bruno: KmL: k-means for longitudinal data (2010)
  11. Lebbah, Mustapha; Benabdeslem, Khalid: Visualization and clustering of categorical data with probabilistic self-organizing map (2010) ioport
  12. Reddy, Chandan K.; Rajaratnam, Bala: Learning mixture models via component-wise parameter smoothing (2010)
  13. Di Zio, Marco; Guarnera, Ugo: Semiparametric predictive mean matching (2009)
  14. Wang, Haixian; Hu, Zilan: On EM estimation for mixture of multivariate $t$-distributions (2009) ioport
  15. Böhning, Dankmar; Seidel, Wilfried; Alfó, Macro; Garel, Bernard; Patilea, Valentin; Walther, Günther: Advances in mixture models (2007)
  16. Di Zio, Marco; Guarnera, Ugo; Luzi, Orietta: Imputation through finite Gaussian mixture models (2007)
  17. Frühwirth-Schnatter, Sylvia: Finite mixture and Markov switching models. (2006)
  18. Wasito, Ito; Mirkin, Boris: Nearest neighbours in least-squares data imputation algorithms with different missing patterns (2006)
  19. Agusta, Yudi; Dowe, David L.: Unsupervised learning of correlated multivariate Gaussian mixture models using MML (2003)
  20. Böhning, Dankmar; Seidel, Wilfried: Editorial: Recent developments in mixture models (Hamburg, July 2001) (2003)

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