Approaches for scaling DBSCAN algorithm to large spatial databases

A new scalable parallel DBSCAN algorithm using the disjoint-set data structure

“…, X p } (one for each of the p threads running in parallel) and each thread t owns partition X t . We divide the algorithm into two segments, local computation (Line 2-18) and merging (Line [19][20][21][22][23][24][25]. Both steps run in parallel.…”

“…This strategy incurs a high communication overhead between the master and slaves, and a low parallel efficiency during the merging process. Other parallelizations using a similar master-slave model include [15], [16], [17], [18], [19], [20]. Among these master-slave approaches, various programming mechanisms have been used, for example, a special parallel programming environment, named skeleton based programming in [17] and parallel virtual machine in [19].…”

“…the density of the neighborhood has to exceed some threshold. A short and brief description based on [5], [9], [20] is given below.…”

“…Note that a cluster can be identified uniquely by starting with any core point of the cluster [20]. The computational complexity of Algorithm 1 is O(n 2 ), where n is the number of points in X.…”

GeoSOM Suite: A Tool for Spatial Clustering

“…CLARANS [6], HEM [7] and DBSCAN/GDBSCAN [8,9] are examples of partitioning methods that are useful for spatial data mining.…”