A new clustering model for skew-symmetric matrices is introduced to analyse flow data. This model aims to find clusters of objects that have a significant flow, interpreted as exchange intensity. The model analyses the within-clusters effects between objects and pro- vides the directions of the flows within clusters. Formally, it is based on the decomposition of the data skew-symmetric matrix into within-cluster components, i.e. the skew-symmetric matrix is decomposed into a sum of diagonal block skew-symmetric matrices. The model is estimated in a least-squares sense through the SVD of the skew-symmetric matrices. An application to the international student mobility is discussed.

A clustering model for flow data: an application to international student mobility

Di Nuzzo C.;
2023-01-01

Abstract

A new clustering model for skew-symmetric matrices is introduced to analyse flow data. This model aims to find clusters of objects that have a significant flow, interpreted as exchange intensity. The model analyses the within-clusters effects between objects and pro- vides the directions of the flows within clusters. Formally, it is based on the decomposition of the data skew-symmetric matrix into within-cluster components, i.e. the skew-symmetric matrix is decomposed into a sum of diagonal block skew-symmetric matrices. The model is estimated in a least-squares sense through the SVD of the skew-symmetric matrices. An application to the international student mobility is discussed.
2023
Flow data, Skew-symmetry, Within-cluster effects
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/582335
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