  The statistical description of Giant Molecular Cloud (GMC) properties relies
heavily on the performance of automatic identification algorithms, which are
often seriously affected by the survey design. The algorithm we designed,
SCIMES (Spectral Clustering for Interstellar Molecular Emission Segmentation),
is able to overcome some of these limitations by considering the cloud
segmentation problem in the broad framework of the graph theory. The
application of the code on the CO(3-2) High Resolution Survey (COHRS) data
allowed for a robust decomposition of more than 12,000 objects in the Galactic
Plane. Together with the wealth of Galactic Plane surveys of the recent years,
this approach will help to open the door to a future, systematic cataloging of
all discrete molecular features of our own Galaxy.
