Codice 09/2010 Titolo Joint Clustering and Alignment of Functional Data: an Application to Vascular Geometries Data 2010-02-26 Autore/i Sangalli, Laura M.; Secchi, Piercesare; Vantini, Simone; Vitelli, Valeria Link Download full text Pubblicato 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. 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.