This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2012 IEEE Fifth International Conference on Cloud Computing
Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition
Honolulu, HI, USA USA
June 24-June 29
ISBN: 978-1-4673-2892-0
DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors.
Index Terms:
Partitioning algorithms,Algorithm design and analysis,Clustering algorithms,Indexes,Scalability,Cloud computing,Data mining,cloud computing,data mining,data clustering,DBSCAN,Map/Reduce,data partition,Hadoop
Citation:
Bi-Ru Dai, I-Chang Lin, "Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition," cloud, pp.59-66, 2012 IEEE Fifth International Conference on Cloud Computing, 2012
Usage of this product signifies your acceptance of the Terms of Use.