Mixture-based clustering for the ordered stereotype model

D. Fernández, R. Arnold and S. Pledger

Computational Statistics & Data Analysis, 2016, vol. 93, issue C, 46-75

Abstract: Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown.

Keywords: Biclustering; Cluster analysis; Dimension reduction; EM-algorithm; Finite mixture model; Fuzzy clustering; Likert scale; Ordinal data; Stereotype model (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:93:y:2016:i:c:p:46-75

DOI: 10.1016/j.csda.2014.11.004

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