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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ke Wang Ping Guo A-Li Luo |
| Copyright Year | 2015 |
| Description | Author affiliation: Key Lab. of Opt. Astron. Nat. Astron. Obs., Beijing, China (A-Li Luo) || Sch. of Comput. Sci. & Technol., Beijing Inst. of Technol., Beijing, China (Ke Wang; Ping Guo) |
| Abstract | Affinity Propagation (AP) algorithm is a useful clustering technique with a lot of noteworthy advantages. It has been successfully applied in many applications. However, this algorithm does not scale for large scale data sets because it requires quadratic computational time and memory usage in the problem size. In this paper, we concentrate on the needs of big data analytics and propose an effective and efficient scheme to decrease the computational complexity and memory usage of AP algorithm. The basic idea of our approach is embedding data points in distance-preserving binary codes and then decomposing the original big data set into a series of small subsets by aggregating similar data points according to their binary codes. The experimental results and the real world astronomical spectral data application demonstrate the effectiveness of our approach quantitatively and visually. |
| Starting Page | 601 |
| Ending Page | 608 |
| File Size | 780450 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479999262 |
| DOI | 10.1109/BigData.2015.7363804 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-29 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Quantization (signal) Clustering algorithms Binary codes Affinity propagation Approximation algorithms Big data Partitioning algorithms Astronomical big data Clustering |
| Content Type | Text |
| Resource Type | Article |
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