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 - Sep 2016

Bibliographical note

Article in press, full citation details will be uploaded when available.
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
- Centre for Trust, Peace and Social Relations - Associate Professor Academic
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 - Matechou, E.

AU - Liu, I.

AU - Fernandez, D.

AU - Farias, Miguel

AU - Gjelsvik, B.

N1 - Article in press, full citation details will be uploaded when available. 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.

PY - 2016/9

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N2 - 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

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

KW - fuzzy clustering

KW - Likert scale

KW - proportional odds

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M3 - Article

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SP - 611

EP - 624

JO - Psychometrika

JF - Psychometrika

SN - 0033-3123

IS - 3

ER -

Biclustering Models for Two-Mode Ordinal Data

Abstract

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Keywords

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Miguel Farias

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