Parallel DBSCAN with Priority R-tree

Author

Chen, Min ; Gao, Xuedong ; Li, HuiFei

Author_Institution

Sch. of Econ. & Manage., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2010

fDate

16-18 April 2010

Firstpage

508

Lastpage

511

Abstract

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.

Keywords

data mining; database management systems; parallel algorithms; pattern clustering; tree data structures; clustering analysis; computer nodes; parallel DBSCAN; parallel algorithm; priority r-tree; Acceleration; Algorithm design and analysis; Clustering algorithms; Data analysis; Environmental economics; Parallel algorithms; Personal communication networks; Scalability; Spatial databases; Technology management; Clustering; algorithm DBSCAN; parallel DBSCAN;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-5263-7

Electronic_ISBN

978-1-4244-5265-1

Type

conf

DOI

10.1109/ICIME.2010.5477926

Filename

5477926