Name
Abstract | ||
|---|---|---|
This paper applied the Methods which based on GEP in compress multi-streams. The contributions of this paper include: 1) giving an introduction to data function finding based on GEP(DFF-GEP), defining the main conception of Multi-Streams, and revealing the map relation in it; 2) putting forward the Compression Algorithm for Multi-Streams according to map relation lied in data between data streams; and 3)providing an experience with the real data and find that (3.1) the compression ratio of the new methods is 120~150 times as the traditional wavelets method, and 35~70 times as the wavelets and coincidence method; (3.2) the relative error of the new method is about 3%, yet maximum relative error is 0.01 by using the traditional relative error standard, the precision is improved from 7% to 15% as compared with the traditional method. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WGEC.2009.26 | WGEC |
Keywords | Field | DocType |
relative error standard,maximum relative error,wavelets method,wavelet transforms,multi-streams,data streams,data compression,traditional method,compression algorithm,traditional relative error standard,coincidence method,relative error,traditional wavelets method,gep,map relation,data function,genetic computing,new method,data stream,data mining,algorithm design and analysis,genetics,gene expression,programming,data models,compression ratio | Data mining,Data modeling,Data stream mining,Algorithm design,Computer science,Compression ratio,Artificial intelligence,Data compression,Machine learning,Approximation error,Wavelet,Wavelet transform | Conference |
ISBN | Citations | PageRank |
978-0-7695-3899-0 | 1 | 0.35 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Ding | 1 | 1 | 0.35 |
| Chang-an Yuan | 2 | 85 | 9.88 |
| Xiao Qin | 3 | 1 | 3.40 |
| Yu-zhong Peng | 4 | 10 | 1.66 |