Automatic data mining by asynchronous parallel evolutionary algorithms
Jiandong Li, Zhuo Kang, Yan Li, Hongqing Cao, Pu Liu · 2002
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). A 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.