Discovery of inexact concepts from structural data

Author

Holder, Lawrence B. ; Cook, Diane J.

Author_Institution

Dept. of Comput. Sci. Eng., Texas Univ., Arlington, TX, USA

Volume

5

Issue

6

fYear

1993

fDate

12/1/1993 12:00:00 AM

Firstpage

992

Lastpage

994

Abstract

Concept discovery in structural data requires the identification of repetitive substructures in the data. A method for discovering substructures in data using an inexact graph match is described. An implementation of the authors´ SUBDUE system that employs an inexact graph match to discover substructures which occur often in the data, but not always in the same form, is described. This inexact substructure discovery can be used to formulate fuzzy concepts, compress the data description, and discover interesting structures in data that are found either in an identical or in a slightly convoluted form. Examples from the domains of scene analysis and chemical compound analysis demonstrate the benefits of the inexact discovery technique

Keywords

deductive databases; graph theory; knowledge acquisition; knowledge based systems; pattern recognition; SUBDUE system; chemical compound analysis; concept discovery; data description; fuzzy concepts; inexact concepts; inexact graph match; inexact substructure discovery; knowledge acquisition; pattern recognition; repetitive substructures; scene analysis; structural data; Chemical analysis; Chemical compounds; Computer science; Data analysis; Data compression; Data mining; Databases; Heuristic algorithms; Image analysis; Psychology;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.250085

Filename

250085