Parallel DBSCAN with Priority R-tree

• Published in: 2010 2nd IEEE International Conference on Information Management and Engineering pp. 508 - 511
• Main Authors: Min Chen, XueDong Gao, HuiFei Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2010
• Subjects: Acceleration; algorithm DBSCAN; Algorithm design and analysis; Clustering; Clustering algorithms; Data analysis; Environmental economics; Parallel algorithms; parallel DBSCAN; Personal communication networks; Scalability; Spatial databases; Technology management
• ISBN: 9781424452637; 1424452635
• DOI: 10.1109/ICIME.2010.5477926

According to the efficiency bottleneck of algorithm DBSCAN, we present P-DBSCAN, a novel parallel version of this algorithm in distributed environment. By separating the database into several parts, the computer nodes carry out clustering independently; after that, the sub-results will be aggregated into one final result. P-DBSCAN achieves good results and much better efficiency than DBSCAN. Experiments show that, P-DBSCAN accelerates more than 40% on one PC, and 60% on two PCs. In addition, the parallel algorithm has much better scalability than DBSCAN, so that it can be used for clustering analysis in huge databases.