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We present in this paper a novel non-parametric approach useful forclustering Markov processes. We introduce a pre-processing step consisting inmapping multivariate independent and identically distributed samples fromrandom variables to a generic non-parametric representation which factorizesdependency and marginal distribution apart without losing any. An associatedmetric is defined where the balance between random variables dependency anddistribution information is controlled by a single parameter. This mixingparameter can be learned or played with by a practitioner, such use isillustrated on the case of clustering financial time series. Experiments,implementation and results obtained on public financial time series are onlineon a web portal \url{http://www.datagrapple.com}.

Comment partitionner automatiquement des marches al\'eatoires ? Avec application \`a la finance quantitative