Analysis of AneuRisk65 data: $k$-mean alignment

Abstract

We describe the $k$-mean alignment procedure, for the joint alignment and clustering of functional data and we apply it to the analysis of the AneuRisk65 data. Thanks to the efficient separation of the variability in phase variability and within/between clusters amplitude variability, we are able to discriminate subjects having aneurysms in different cerebral districts and identifying different morphological shapes of Inner Carotid Arteries, unveiling a strong association between arteries morphologies and the aneurysmal pathology.