Knowledge extraction methods for complex processes operating under uncertainty

Author

Vatchova, Boriana ; Gegov, Alexander

Author_Institution

Inst. of Inf. & Commun. Technol., Sofia, Bulgaria

fYear

2012

fDate

6-8 Sept. 2012

Abstract

This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The proposed approach which can be applied in this case is based on artificial intelligence methods.

Keywords

data mining; multivalued logic; probability; random processes; stochastic processes; artificial intelligence method; complex process; data mining method; knowledge extraction method; multivalued logic; probability function; process model; random function theory; stochastic environmental factor; uncertain operation; Computational modeling; Data mining; Data models; Knowledge based systems; Production; Uncertainty; functions of multi-valued logic; knowledge bases; logical values; multivalued logical and probability functions; network models; relations; sequence data sets;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (IS), 2012 6th IEEE International Conference

Conference_Location

Sofia

Print_ISBN

978-1-4673-2276-8

Type

conf

DOI

10.1109/IS.2012.6335183

Filename

6335183