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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qingyu Zhai Dongfeng Yuan Haixia Zhang Kai Gao |
| Copyright Year | 2014 |
| Description | Author affiliation: Shandong Univ., Jinan, China (Qingyu Zhai; Dongfeng Yuan; Haixia Zhang; Kai Gao) |
| Abstract | To improve the searching performance to find better initial cluster centers and the calculating performance to process massive data in high dimensions, for PSO K-means, a brand new hybrid data clustering algorithm named Parallelization of OBL based PSO K-means Algorithm with the OpenCL Architecture (POPK) is introduced in this paper. In POPK, Opposition-based Learning (OBL) is applied to improve the global searching ability of Particle Swarm Optimization (PSO) in search of better initial centers of clusters for K-means while Open Computing Language (OpenCL) is introduced to parallelize the OBL-based PSO K-means and to enhance the calculating ability such that an obvious speed-up is obtained. Experimental results indicate that both effectiveness and efficiency of POPK is acceptably improved compared with standard PSO K-means. |
| Starting Page | 714 |
| Ending Page | 719 |
| File Size | 242212 |
| Page Count | 6 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975924 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis K-means Clustering algorithms Graphics processing units Computer architecture OBL Vectors OpenCL Particle swarm optimization Kernel PSO |
| Content Type | Text |
| Resource Type | Article |
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