Joint Clustering and Alignment of Functional Data: an Application to Vascular Geometries
Keywords
Statistical learning
Computational Medicine for the Cardiocirculatory System
Code:
09/2010
Title:
Joint Clustering and Alignment of Functional Data: an Application to Vascular Geometries
Date:
Friday 26th February 2010
Author(s):
Sangalli, Laura M.; Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria
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Abstract:
We show an application of the k-mean alignment method presented in Sangalli et al. (2010b). This is a method for jointly clustering and aligning
functions that puts in a unique framework two widely used methods of functional data analysis: Procrustes continuous alignment and functional
k-mean clustering. These two methods turn out to be two special cases of the new method. In detail we use this algorithm to analyze 65 internal
carotid arteries (ICA) in relation to the presence and rupture of cerebral aneurysms. Some interesting issues, amenable of a biological interpretation and pointed out by the analysis, are briefly discussed.
This report, or a modified version of it, has been also submitted to, or published on
Laura M. Sangalli, Piercesare Secchi, Simone Vantini and Valeria Vitelli (2012), Joint Clustering and Alignment of Functional Data: an Application to Vascular Geometries, in Advanced Statistical Methods for the Analysis of Large Data-Sets, Springer, pp 33-43.
Laura M. Sangalli, Piercesare Secchi, Simone Vantini and Valeria Vitelli (2012), Joint Clustering and Alignment of Functional Data: an Application to Vascular Geometries, in Advanced Statistical Methods for the Analysis of Large Data-Sets, Springer, pp 33-43.