  We present a new algorithm, CAMIRA, to identify clusters of galaxies in
wide-field imaging survey data. We base our algorithm on the stellar population
synthesis model to predict colours of red-sequence galaxies at a given redshift
for an arbitrary set of bandpass filters, with additional calibration using a
sample of spectroscopic galaxies to improve the accuracy of the model
prediction. We run the algorithm on ~11960 deg^2 of imaging data from the Sloan
Digital Sky Survey (SDSS) Data Release 8 to construct a catalogue of 71743
clusters in the redshift range 0.1<z<0.6 with richness after correcting for the
incompleteness of the richness estimate greater than 20. We cross-match the
cluster catalogue with external cluster catalogues to find that our photometric
cluster redshift estimates are accurate with low bias and scatter, and that the
corrected richness correlates well with X-ray luminosities and temperatures. We
use the publicly available Canada-France-Hawaii Telescope Lensing Survey
(CFHTLenS) shear catalogue to calibrate the mass-richness relation from stacked
weak lensing analysis. Stacked weak lensing signals are detected significantly
for 8 subsamples of the SDSS clusters divided by redshift and richness bins,
which are then compared with model predictions including miscentring effects to
constrain mean halo masses of individual bins. We find the richness correlates
well with the halo mass, such that the corrected richness limit of 20
corresponds to the cluster virial mass limit of about 1 \times 10^14 M_Sun/h
for the SDSS DR8 cluster sample.
