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 catalogues shows that >~90 per cent of true groups with halo masses M_h_>~_10^12^M_{sun}_h^-1^ are successfully identified, and that the fraction of contaminants is smaller than 10 per cent. The standard deviation in the halo mass estimation is smaller than 0.25dex at all masses. We apply our group finder to zCOSMOS-bright and describe basic properties of the group catalogue obtained.