Title :
Automatic data mining by asynchronous parallel evolutionary algorithms
Author :
Li, Jiandong ; Kang, Zhuo ; Li, Yan ; Cao, Hongqing ; Liu, Pu
Author_Institution :
Somiya Int. Inc., San Jose, CA, USA
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). 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
Keywords :
data mining; database theory; differential equations; evolutionary computation; parallel algorithms; very large databases; APHEMA; asynchronous parallel evolutionary modeling algorithm; data mining; database knowledge discovery; difference equations; differential equations; dynamic data; high-level knowledge; Concurrent computing; Data mining; Databases; Differential equations; Evolutionary computation; H infinity control; Laboratories; Parallel algorithms; Predictive models; Software engineering;
Conference_Titel :
Technology of Object-Oriented Languages and Systems, 2001. TOOLS 39. 39th International Conference and Exhibition on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-7695-1251-8
DOI :
10.1109/TOOLS.2001.941664
