  In this article, a new method, called FWP, is proposed for clustering
longitudinal curves. In the proposed method, clusters of mean functions are
identified through a weighted concave pairwise fusion method. The EM algorithm
and the alternating direction method of multiplier algorithm are combined to
estimate the group structure, mean functions and the principal components
simultaneously. The proposed method also allows to incorporate the prior
neighborhood information to have more meaningful groups by adding pairwise
weights in the pairwise penalties. In the simulation study, the performance of
the proposed method is compared to some existing clustering methods in terms of
the accuracy for estimating the number of subgroups and mean functions. The
results suggest that ignoring covariance structure will have a great effect on
the performance of estimating the number of groups and estimating accuracy. The
effect of including pairwise weights is also explored in a spatial lattice
setting to take consideration of the spatial information. The results show that
incorporating spatial weights will improve the performance. A real example is
used to illustrate the proposed method.
