  In this paper, we apply the machine learning clustering algorithm Density
Based Spatial Clustering of Applications with Noise (DBSCAN) to study the
membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244,
NGC~3293, NGC~6913, NGC~7142, IC~1805, NGC~6231, NGC~2243, NGC 6451, NGC 6005
and NGC 6583) based on Gaia DR3 Data. This sample of clusters spans a variety
of parameters like age, metallicity, distance, extinction and a wide parameter
space in proper motions and parallaxes. We obtain reliable cluster members
using DBSCAN as faint as $G \sim 20$ mag and also in the outer regions of
clusters. With our revised membership list, we plot color-magnitude diagrams
and we obtain cluster parameters for our sample using ASteCA and compare it
with the catalog values. We also validate our membership sample by
spectroscopic data from APOGEE and GALAH for the available data. This paper
demonstrates the effectiveness of DBSCAN in membership determination of
clusters.
