A classification model for three-way dissimilarity data is presented including the idea to identify simultaneously a fuzzy partition of the set of occasions in classes of similar proximity matrices and, within each class, to fit a 2-ultrametric matrix (i.e., with at most two different off-diagonal values) that represents the best consensus partition of the objects. We propose a data reduction approach that induces a specific covariance structure inside each class of the fuzzy partition. The model is formalized by a constrained maximum likelihood mixed-integer problem and a numerical method, based on ECM strategy, is also described.

A classification model for three-way dissimilarity data is presented including the idea to identify simultaneously a fuzzy partition of the set of occasions in classes of similar proximity matrices and, within each class, to fit a 2-ultrametric matrix (i.e., with at most two different off-diagonal values) that represents the best consensus partition of the objects. We propose a data reduction approach that induces a specific covariance structure inside each class of the fuzzy partition. The model is formalized by a constrained maximum likelihood mixed-integer problem and a numerical method, based on ECM strategy, is also described.

Classification of Three-way Proximity Data

BOCCI, Laura;VICARI, Donatella;VICHI, Maurizio
2002

Abstract

A classification model for three-way dissimilarity data is presented including the idea to identify simultaneously a fuzzy partition of the set of occasions in classes of similar proximity matrices and, within each class, to fit a 2-ultrametric matrix (i.e., with at most two different off-diagonal values) that represents the best consensus partition of the objects. We propose a data reduction approach that induces a specific covariance structure inside each class of the fuzzy partition. The model is formalized by a constrained maximum likelihood mixed-integer problem and a numerical method, based on ECM strategy, is also described.
9788372521347
A classification model for three-way dissimilarity data is presented including the idea to identify simultaneously a fuzzy partition of the set of occasions in classes of similar proximity matrices and, within each class, to fit a 2-ultrametric matrix (i.e., with at most two different off-diagonal values) that represents the best consensus partition of the objects. We propose a data reduction approach that induces a specific covariance structure inside each class of the fuzzy partition. The model is formalized by a constrained maximum likelihood mixed-integer problem and a numerical method, based on ECM strategy, is also described.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/407009
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