GINDCLUS: Generalized INDCLUS with External Information

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

Psychometrika, 2017, vol. 82, issue 2, No 5, 355-381

Abstract: Abstract A Generalized INDCLUS model, termed GINDCLUS, is presented for clustering three-way two-mode proximity data. In order to account for the heterogeneity of the data, both a partition of the subjects into homogeneous classes and a covering of the objects into groups are simultaneously determined. Furthermore, the availability of information which is external to the three-way data is exploited to better account for such heterogeneity: the weights of both classifications are linearly linked to external variables allowing for the identification of meaningful classes of subjects and groups of objects. The model is fitted in a least-squares framework, and an efficient Alternating Least-Squares algorithm is provided. An extensive simulation study and an application on benchmark data are also presented.

Keywords: clustering; external information; INDCLUS; three-way proximity data (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11336-016-9526-9

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