Biclustering Models for Two-Mode Ordinal Data

E. Matechou; I. Liu; D. Fernandez; Miguel Farias; B. Gjelsvik

doi:10.1007/s11336-016-9503-3

Biclustering Models for Two-Mode Ordinal Data

E. Matechou, I. Liu, D. Fernandez, Miguel Farias, B. Gjelsvik

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Abstract

The work in this paper introduces finite mixture models that can be used tosimultaneously cluster the rows and columns of ordinal categorical responsedata. Model-fitting is performed using the EM algorithm and a fuzzy allocationof rows and columns to corresponding clusters is obtained. The clusteringability of the models is evaluated, and compared to that of k-means,in a simulation study, and demonstrated using two real data sets.

The final publication is available at Springer via http://dx.doi.org/10.1007/s11336-016-9503-3

Original language	English
Pages (from-to)	611-624
Journal	Psychometrika
Volume	81
Issue number	3
Early online date	21 Jun 2016
DOIs	https://doi.org/10.1007/s11336-016-9503-3
Publication status	Published - Sept 2016

Bibliographical note

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Keywords

EM algorithm
fuzzy clustering
Likert scale
proportional odds

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10.1007/s11336-016-9503-3

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Miguel Farias
- Research Centre for Peace and Security - Associate Professor
Person: Teaching and Research

Cite this

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title = "Biclustering Models for Two-Mode Ordinal Data",

abstract = "The work in this paper introduces finite mixture models that can be used tosimultaneously cluster the rows and columns of ordinal categorical responsedata. Model-fitting is performed using the EM algorithm and a fuzzy allocationof rows and columns to corresponding clusters is obtained. The clusteringability of the models is evaluated, and compared to that of k-means,in a simulation study, and demonstrated using two real data sets.The final publication is available at Springer via http://dx.doi.org/10.1007/s11336-016-9503-3",

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AU - Liu, I.

AU - Fernandez, D.

AU - Farias, Miguel

AU - Gjelsvik, B.

N1 - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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AB - The work in this paper introduces finite mixture models that can be used tosimultaneously cluster the rows and columns of ordinal categorical responsedata. Model-fitting is performed using the EM algorithm and a fuzzy allocationof rows and columns to corresponding clusters is obtained. The clusteringability of the models is evaluated, and compared to that of k-means,in a simulation study, and demonstrated using two real data sets.The final publication is available at Springer via http://dx.doi.org/10.1007/s11336-016-9503-3

KW - EM algorithm

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KW - Likert scale

KW - proportional odds

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JO - Psychometrika

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Biclustering Models for Two-Mode Ordinal Data

Abstract

Bibliographical note

Keywords

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Miguel Farias

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