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

Laura Bocci () and Donatella Vicari ()
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Laura Bocci: Sapienza University of Rome
Donatella Vicari: Sapienza University of Rome

Psychometrika, 2019, vol. 84, issue 4, No 2, 985 pages

Abstract: 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: clustering; INDCLUS; individual partitions; three-way proximity data (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11336-019-09686-1

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