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Advances in Data Analysis and Classification

, Volume 13, Issue 1, pp 117–143 | Cite as

Finite mixture biclustering of discrete type multivariate data

  • Daniel FernándezEmail author
  • Richard Arnold
  • Shirley Pledger
  • Ivy Liu
  • Roy Costilla
Regular Article

Abstract

Many of the methods which deal with clustering in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. In general, it is not possible to use statistical inferences or select the appropriateness of a model via information criteria with these techniques because there is no underlying probability model. This article summarizes some recent model-based methodologies for matrices of binary, count, and ordinal data, which are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. The model parameter can be constructed from a linear predictor of parameters and covariates through link functions. This likelihood-based one-mode and two-mode fuzzy clustering provides maximum likelihood estimation of parameters and the options of using likelihood information criteria for model comparison. Additionally, a Bayesian approach is presented in which the parameters and the number of clusters are estimated simultaneously from their joint posterior distribution. Visualization tools focused on ordinal data, the fuzziness of the clustering structures, and analogies of various standard plots used in the multivariate analysis are presented. Finally, a set of future extensions is enumerated.

Keywords

Classification EM algorithm Fuzzy clustering Mixture models Ordinal data RJMCMC Visualisation tools 

Mathematics Subject Classification

62F15 62F86 62H12 62H30 62H86 

Notes

Acknowledgements

This work was supported by the Marsden Fund on “Dimension reduction for mixed type multivariate data” (Award Number E2987-3648) from New Zealand Government funding, administrated by the Royal Society of New Zealand.

Supplementary material

11634_2018_324_MOESM1_ESM.pdf (179 kb)
Supplementary material 1 (pdf 179 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de DéuCIBERSAMSant Boi de LlobregatSpain
  2. 2.School of Mathematics and StatisticsVictoria University of WellingtonWellingtonNew Zealand
  3. 3.Institute for Molecular BioscienceUniversity of QueenslandBrisbaneAustralia

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