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

  • Laura Bocci
  • Donatella VicariEmail author
Article
  • 3 Downloads

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 

Notes

Acknowledgements

The authors are grateful to the Associate Editor and referees for their valuable comments and suggestions which greatly improved the presentation and content of the first version.

Supplementary material

11336_2019_9686_MOESM1_ESM.zip (280 kb)
Supplementary material 1 (zip 279 KB)

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Copyright information

© The Psychometric Society 2019

Authors and Affiliations

  1. 1.Department of Communication and Social ResearchSapienza University of RomeRomeItaly
  2. 2.Department of Statistical SciencesSapienza University of RomeRomeItaly

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