  Clustering analysis of functional data, which comprises observations that
evolve continuously over time or space, has gained increasing attention across
various scientific disciplines. Practical applications often involve functional
data that are contaminated with measurement errors arising from imprecise
instruments, sampling errors, or other sources. These errors can significantly
distort the inherent data structure, resulting in erroneous clustering
outcomes. In this paper, we propose a simulation-based approach designed to
mitigate the impact of measurement errors. Our proposed method estimates the
distribution of functional measurement errors through repeated measurements.
Subsequently, the clustering algorithm is applied to simulated data generated
from the conditional distribution of the unobserved true functional data given
the observed contaminated functional data, accounting for the adjustments made
to rectify measurement errors. We illustrate through simulations show that the
proposed method has improved numerical performance than the naive methods that
neglect such errors. Our proposed method was applied to a childhood obesity
study, giving more reliable clustering results
