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We present a new technique designed to take full advantage of the highdimensionality (photometric, astrometric, temporal) of the DANCe survey toderive self-consistent and robust membership probabilities of the Pleiadescluster. We aim at developing a methodology to infer membership probabilitiesto the Pleiades cluster from the DANCe multidimensional astro-photometric dataset in a consistent way throughout the entire derivation. The determination ofthe membership probabilities has to be applicable to censored data and mustincorporate the measurement uncertainties into the inference procedure.  We use Bayes' theorem and a curvilinear forward model for the likelihood ofthe measurements of cluster members in the colour-magnitude space, to inferposterior membership probabilities. The distribution of the cluster membersproper motions and the distribution of contaminants in the fullmultidimensional astro-photometric space is modelled with amixture-of-Gaussians likelihood. We analyse several representation spacescomposed of the proper motions plus a subset of the available magnitudes andcolour indices. We select two prominent representation spaces composed ofvariables selected using feature relevance determination techniques based inRandom Forests, and analyse the resulting samples of high probabilitycandidates. We consistently find lists of high probability (p > 0.9975)candidates with $\approx$ 1000 sources, 4 to 5 times more than obtained in themost recent astro-photometric studies of the cluster.  The methodology presented here is ready for application in data sets thatinclude more dimensions, such as radial and/or rotational velocities, spectralindices and variability.

Cluster membership probabilities from proper motions and multiwavelength photometric catalogues: I. Method and application to the Pleiades cluster