Abstract
The clustering and classification for functional data with misaligned problems has drawn much attention in the last decade. Most methods do the clustering/classification after those functional data being registered and there has been little research using both functional and scalar variables. In this talk, I will discuss a new approach allowing the use of both types of variables and also allowing simultaneous registration and clustering/classification. Numerical results based on both simulated data and real data will be presented.
