  Identifying galaxy groups from redshift surveys of galaxies plays an
important role in connecting galaxies with the underlying dark matter
distribution. Current and future high-$z$ spectroscopic surveys, usually
incomplete in redshift sampling, present both opportunities and challenges to
identifying groups in the high-$z$ Universe. We develop a group finder that is
based on incomplete redshift samples combined with photometric data, using a
machine learning method to assign halo masses to identified groups. Test using
realistic mock catalogs shows that $\gtrsim 90\%$ of true groups with halo
masses $\rm M_h \gtrsim 10^{12} M_{\odot}/h$ are successfully identified, and
that the fraction of contaminants is smaller than $10\%$. The standard
deviation in the halo mass estimation is smaller than 0.25 dex at all masses.
We apply our group finder to zCOSMOS-bright and describe basic properties of
the group catalog obtained.
