  The detection and parametrization of molecular clumps is the first step in
studying them. We propose a method based on Local Density Clustering algorithm
while physical parameters of those clumps are measured using the Multiple
Gaussian Model algorithm. One advantage of applying the Local Density
Clustering to the clump detection and segmentation, is the high accuracy under
different signal-to-noise levels. The Multiple Gaussian Model is able to deal
with overlapping clumps whose parameters can be derived reliably. Using
simulation and synthetic data, we have verified that the proposed algorithm
could characterize the morphology and flux of molecular clumps accurately. The
total flux recovery rate in $^{13}\rm CO$ (J=1-0) line of M16 is measured as
90.2\%. The detection rate and the completeness limit are 81.7\% and 20 K km s$
^{-1} $ in $^{13}\rm CO$ (J=1-0) line of M16, respectively.
