title: GINDCLUS: Generalized INDCLUS with External Information.
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.
authors.affiliation: Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy.
authors.forename: Laura
authors.initials: L
authors.lastname: Bocci
authors.affiliation: Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy. donatella.vicari@uniroma1.it.
authors.forename: Donatella
authors.identifier: 0000-0002-2821-2991
authors.initials: D
authors.lastname: Vicari
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D006801:Humans; D016018:Least-Squares Analysis; D011594:Psychometrics
keywords: INDCLUS; clustering; external information; three-way proximity data