  We introduce a new clustering algorithm, MulGuisin (MGS), that can identify
distinct galaxy over-densities using topological information from the galaxy
distribution. This algorithm was first introduced in an LHC experiment as a Jet
Finder software, which looks for particles that clump together in close
proximity. The algorithm preferentially considers particles with high energies
and merges them only when they are closer than a certain distance to create a
jet. MGS shares some similarities with the minimum spanning tree (MST) since it
provides both clustering and network-based topology information. Also, similar
to the density-based spatial clustering of applications with noise (DBSCAN),
MGS uses the ranking or the local density of each particle to construct
clustering. In this paper, we compare the performances of clustering algorithms
using controlled data and some realistic simulation data as well as the SDSS
observation data, and we demonstrate that our new algorithm finds networks most
efficiently and defines galaxy networks in a way that most closely resembles
human vision.
