Abstract: Clustering algorithms help to understand the hidden
information present in datasets. A dataset may contain intrinsic and
nested clusters, the detection of which is of utmost importance. This
paper presents a Distributed Grid-based Density Clustering algorithm
capable of identifying arbitrary shaped embedded clusters as well as
multi-density clusters over large spatial datasets. For handling
massive datasets, we implemented our method using a 'sharednothing'
architecture where multiple computers are interconnected
over a network. Experimental results are reported to establish the
superiority of the technique in terms of scale-up, speedup as well as
cluster quality.