A Distributed Algorithm For Intrinsic Cluster Detection Over Large Spatial Data
Rosy Sarmah, Rosy Das, Dhruba K. Bhattacharyya · Zenodo (CERN European Organization for Nuclear Research) · 2008
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.