  Improving the future of healthcare starts by better understanding the current
actual practices in hospitals. This motivates the objective of discovering
typical care pathways from patient data. Revealing homogeneous groups of care
pathways can be achieved through clustering. The difficulty in clustering care
pathways, represented by sequences of timestamped events, lies in defining a
semantically appropriate metric and clustering algorithms.
  In this article, we adapt two methods developed for time series to time
sequences: the drop-DTW metric and the DBA approach for the construction of
averaged time sequences. These methods are then applied in clustering
algorithms to propose original and sound clustering algorithms for timed
sequences.
  This approach is experimented with and evaluated on synthetic and real use
cases.
