  Trust calibration is necessary to ensure appropriate user acceptance in
advanced automation technologies. A significant challenge to achieve trust
calibration is to quantitatively estimate human trust in real-time. Although
multiple trust models exist, these models have limited predictive performance
partly due to individual differences in trust dynamics. A personalized model
for each person can address this issue, but it requires a significant amount of
data for each user. We present a methodology to develop customized model by
clustering humans based on their trust dynamics. The clustering-based method
addresses the individual differences in trust dynamics while requiring
significantly less data than personalized model. We show that our
clustering-based customized models not only outperform the general model based
on entire population, but also outperform simple demographic factor-based
customized models. Specifically, we propose that two models based on
``confident'' and ``skeptical'' group of participants, respectively, can
represent the trust behavior of the population. The ``confident'' participants,
as compared to the ``skeptical'' participants, have higher initial trust
levels, lose trust slower when they encounter low reliability operations, and
have higher trust levels during trust-repair after the low reliability
operations. In summary, clustering-based customized models improve trust
prediction performance for further trust calibration considerations.
