Joint Clustering and Alignment of Functional Data: An Application to Vascular Geometries

Abstract

We show an application of the k-mean alignment method presented in Sangalli et al. (Comput. Stat. Data Anal. 54:1219–1233). This is a method for the joint clustering and alignment of functional data, that sets 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 in relation to the presence and rupture of cerebral aneurysms. Some interesting issues pointed out by the analysis and amenable of a biological interpretation are briefly discussed.