Finite mixture biclustering of discrete type multivariate data

Daniel Fernández (), Richard Arnold, Shirley Pledger, Ivy Liu and Roy Costilla
Additional contact information
Daniel Fernández: CIBERSAM
Richard Arnold: Victoria University of Wellington
Shirley Pledger: Victoria University of Wellington
Ivy Liu: Victoria University of Wellington
Roy Costilla: University of Queensland

Advances in Data Analysis and Classification, 2019, vol. 13, issue 1, No 6, 117-143

Abstract: 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; 62F15; 62F86; 62H12; 62H30; 62H86 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-018-0324-3

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