Large data sets organized into a three-way proximity array are generally difficult to comprehend and specific techniques are necessary to extract relevant information. The existing classification methodologies for dissimilarities between objects collected in different occasions assume a unique common underlying classification structure. However, since the objects' clustering structure often changes along the occasions, the use of a single classification to reconstruct the taxonomic information frequently appears quite unrealistic. The methodology proposed here models the dissimilarities in a likelihood framework. The goal is to identify a (secondary) partition of the occasions in homogeneous classes and, simultaneously, a (primary) consensus partition of the objects within each of such classes. Furthermore, a class-specific dimensionality reduction operator is also included which allows to identify classes of occasions such that the within-class variability is minimized. The model is formalized as a finite mixture of multivariate normal distributions and solved by a numerical method based on ECM strategy. (C) 2005 Elsevier B.V. All rights reserved.

A mixture model for the classification of three-way proximity data / Bocci, Laura; Vicari, Donatella; Vichi, Maurizio. - In: COMPUTATIONAL STATISTICS & DATA ANALYSIS. - ISSN 0167-9473. - STAMPA. - 50:7(2006), pp. 1625-1654. [10.1016/j.csda.2005.02.007]

A mixture model for the classification of three-way proximity data

BOCCI, Laura;VICARI, Donatella;VICHI, Maurizio
2006

Abstract

Large data sets organized into a three-way proximity array are generally difficult to comprehend and specific techniques are necessary to extract relevant information. The existing classification methodologies for dissimilarities between objects collected in different occasions assume a unique common underlying classification structure. However, since the objects' clustering structure often changes along the occasions, the use of a single classification to reconstruct the taxonomic information frequently appears quite unrealistic. The methodology proposed here models the dissimilarities in a likelihood framework. The goal is to identify a (secondary) partition of the occasions in homogeneous classes and, simultaneously, a (primary) consensus partition of the objects within each of such classes. Furthermore, a class-specific dimensionality reduction operator is also included which allows to identify classes of occasions such that the within-class variability is minimized. The model is formalized as a finite mixture of multivariate normal distributions and solved by a numerical method based on ECM strategy. (C) 2005 Elsevier B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/232129
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