  Open clusters are central elements of our understanding of the Galactic disk
evolution, as an accurate determination of their parameters leads to an
unbiased picture of our Galaxy's structure. Extending the analysis towards
fainter magnitudes in cluster sequences has a significant impact on the derived
fundamental parameters, such as extinction and total mass. We perform a
homogeneous analysis of six open stellar clusters in the Galactic disk using
kinematic and photometric information from the Gaia DR2 and VVV surveys:
NGC6067, NGC6259, NGC4815, Pismis18, Trumpler23, and Trumpler20. We implement
two coarse-to-fine characterization methods: first, we employ Gaussian mixture
models to tag fields around each open cluster in the proper motion space, and
then we apply an unsupervised machine learning method to make the membership
assignment to each cluster. For the studied clusters, with ages in the
$\sim$120-1900 Myr range, we report an increase of $\sim$45 % new member
candidates on average in our sample. The data-driven selection approach of
cluster members makes our catalog a valuable resource for testing stellar
evolutionary models and for assessing the cluster low-to-intermediate mass
populations. This study is the first of a series intended to homogeneously
reveal open cluster near-infrared sequences.
