The use of cultural algorithms with evolutionary programming to control the data mining of large-scale spatio-temporal databases

Author

Reynolds, Robert ; Al-Shehri, Hasan

Author_Institution

Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA

Volume

5

fYear

1997

fDate

12-15 Oct 1997

Firstpage

4098

Abstract

We use an evolutionary computational approach based upon cultural algorithms to guide the incremental learning decision trees by ITI. The results are compared to those produced by ITI itself for a complex real-world database. The results suggest that ITI can indeed produce optimal trees in some cases, and can produce optimal trees using an evolutionary approach in others

Keywords

database theory; deductive databases; genetic algorithms; knowledge acquisition; learning (artificial intelligence); temporal databases; trees (mathematics); very large databases; visual databases; ITI; complex real-world database; cultural algorithms; data mining; deductive database; evolutionary computational approach; evolutionary programming; incremental learning decision trees; large-scale spatio-temporal databases; optimal trees; Computer science; Cultural differences; Data mining; Databases; Decision trees; Entropy; Genetic programming; Large-scale systems; Machine learning algorithms; Partitioning algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1062-922X

Print_ISBN

0-7803-4053-1

Type

conf

DOI

10.1109/ICSMC.1997.637338

Filename

637338