  We introduce MOSCITO (MOlecular Dynamics Subspace Clustering with Temporal
Observance), a subspace clustering for molecular dynamics data. MOSCITO groups
those timesteps of a molecular dynamics trajectory together into clusters in
which the molecule has similar conformations. In contrast to state-of-the-art
methods, MOSCITO takes advantage of sequential relationships found in time
series data. Unlike existing work, MOSCITO does not need a two-step procedure
with tedious post-processing, but directly models essential properties of the
data. Interpreting clusters as Markov states allows us to evaluate the
clustering performance based on the resulting Markov state models. In
experiments on 60 trajectories and 4 different proteins, we show that the
performance of MOSCITO achieves state-of-the-art performance in a novel
single-step method. Moreover, by modeling temporal aspects, MOSCITO obtains
better segmentation of trajectories, especially for small numbers of clusters.
