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ROOTCLUS: Searching for "ROOT CLUSters" in Three-Way Proximity Data
Laura Bocci1, Donatella Vicari2
1Department of Communication and Social Research, Sapienza University of Rome, Rome, Italy.
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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