We present a {recently} developed clustering method and specify it
for the problem of identification of metastable conformations in
{non-equilibrium}
biomolecular time
series. The approach is based on variational minimization of
some novel regularized clustering functional. In context of conformational analysis,
it allows to combine {the features of} standard
\emph{geometrical clustering techniques} (like the K-Means
algorithm), \emph{dimension reduction methods} (like principle
component analysis (PCA)) and \emph{dynamical machine learning
approaches} like Hidden Markov Models (HMMs). In contrast to the
HMM-based approaches, no a priori assumptions about Markovianity
of the underlying process and regarding probability distribution
of the observed data are needed. The application of the
computational framework is exemplified by means of conformational
analysis of some penta-peptide torsion angle time series from a
molecular dynamics simulation.
Comparison of different versions of the presented algorithm is
performed wrt. the \emph{metastability} and \emph{geometrical
resolution} of the resulting conformations
