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ROOTCLUS: Searching for “ROOT CLUSters” in Three-Way Proximity Data

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

In the context of three-way proximity data, an INDCLUS-type model is presented to address the issue of subject heterogeneity regarding the perception of object pairwise similarity. A model, termed ROOTCLUS, is presented that allows for the detection of a subset of objects whose similarities are described in terms of non-overlapping clusters (ROOT CLUSters) common across all subjects. For the other objects, Individual partitions, which are subject specific, are allowed where clusters are linked one-to-one to the Root clusters. A sound ALS-type algorithm to fit the model to data is presented. The novel method is evaluated in an extensive simulation study and illustrated with empirical data sets.

Keywords

clusteringINDCLUSindividual partitionsthree-way proximity data
Type
Original Paper
Information
Psychometrika , Volume 84 , Issue 4 , December 2019 , pp. 941 - 985
Copyright
Copyright © 2019 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-019-09686-1) contains supplementary material, which is available to authorized users.

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