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GINDCLUS: Generalized INDCLUS with External Information

Published online by Cambridge University Press:  01 January 2025

Laura Bocci  and
Donatella Vicari Open the ORCID record for Donatella Vicari [Opens in a new window]
Laura Bocci
Affiliation:
Sapienza University of Rome
Donatella Vicari*
Affiliation:
Sapienza University of Rome
*
Correspondence should be made to Donatella Vicari, Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy. Email: donatella.vicari@uniroma1.it
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Abstract

A Generalized INDCLUS model, termed GINDCLUS, is presented for clustering three-way two-mode proximity data. In order to account for the heterogeneity of the data, both a partition of the subjects into homogeneous classes and a covering of the objects into groups are simultaneously determined. Furthermore, the availability of information which is external to the three-way data is exploited to better account for such heterogeneity: the weights of both classifications are linearly linked to external variables allowing for the identification of meaningful classes of subjects and groups of objects. The model is fitted in a least-squares framework, and an efficient Alternating Least-Squares algorithm is provided. An extensive simulation study and an application on benchmark data are also presented.

Keywords

clusteringexternal informationINDCLUSthree-way proximity data
Type
Original Paper
Information
Psychometrika , Volume 82 , Issue 2 , June 2017 , pp. 355 - 381
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (doi:10.1007/s11336-016-9526-9) contains supplementary material, which is available to authorized users.

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