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.

Rootclus. Searching for "ROOT CLUSters" in three-way proximity data / Bocci, Laura; Vicari, Donatella. - In: PSYCHOMETRIKA. - ISSN 0033-3123. - (2019), pp. 1-45. [10.1007/s11336-019-09686-1]

Rootclus. Searching for "ROOT CLUSters" in three-way proximity data

Bocci, Laura;Vicari, Donatella
2019

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1313418
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