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

fYear

2001

fDate

2001

Firstpage

99

Lastpage

106

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Technology of Object-Oriented Languages and Systems, 2001. TOOLS 39. 39th International Conference and Exhibition on

Conference_Location

Santa Barbara, CA

ISSN

1530-2067

Print_ISBN

0-7695-1251-8

Type

conf

DOI

10.1109/TOOLS.2001.941664

Filename

941664