loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS39)
Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms
Santa Barbara, California
July 29-August 03
ISBN: 0-7695-1251-8
ASCII Text
x 
 
Jiandong Li, Zhuo Kang, Yan Li, Hongqing Cao, Pu Liu, "Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms," Technology of Object-Oriented Languages, International Conference on, pp. 0099, 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS39), 2001.
 
 
BibTex
x
 
@article{ 10.1109/TOOLS.2001.941664,
author = {Jiandong Li and Zhuo Kang and Yan Li and Hongqing Cao and Pu Liu},
title = {Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms},
journal ={Technology of Object-Oriented Languages, International Conference on},
volume = {0},
year = {2001},
isbn = {0-7695-1251-8},
pages = {0099},
doi = {http://doi.ieeecomputersociety.org/10.1109/TOOLS.2001.941664},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Technology of Object-Oriented Languages, International Conference on
TI - Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms
SN - 0-7695-1251-8
SP
EP
A1 - Jiandong Li,
A1 - Zhuo Kang,
A1 - Yan Li,
A1 - Hongqing Cao,
A1 - Pu Liu,
PY - 2001
KW - Data Mining
KW - Asynchronous parallel algorithm
KW - knowledge discovery
KW - evolutionary modeling
VL - 0
JA - Technology of Object-Oriented Languages, International Conference on
ER -
 
Jiandong Li, Somiya International, Inc
Zhuo Kang, Wuhan University
Yan Li, Wuhan University
Hongqing Cao, Wuhan University
Pu Liu, Wuhan University
Abstract: How to discover high-level knowledge modeled by complicated functions, ordinary differential equations and difference equations in databases automatically is a very important and difficult task in KDD research. In this paper, high-level knowledge modeled by ordinary differential equations (ODEs) is discovered in dynamic data automatically by an asynchronous parallel evolutionary modeling algorithm (APHEMA). Numerical example is used to demonstrate the potential of APEA. The results show that the dynamic models discovered automatically in dynamic data by computer sometimes can compare with the models discovered by human.
Index Terms:
Data Mining, Asynchronous parallel algorithm, knowledge discovery, evolutionary modeling
Citation:
Jiandong Li, Zhuo Kang, Yan Li, Hongqing Cao, Pu Liu, "Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms," tools, pp.0099, 39th International Conference and Exhibition on Technology of Object-Oriented Languages and Systems (TOOLS39), 2001
Usage of this product signifies your acceptance of the Terms of Use.