An empirical study of domain knowledge and its benefits to substructure discovery

Discovering repetitive, interesting, and functional substructures in a structural database improves the ability to interpret and compress the data. However, scientists working with a database in their area of expertise often search for predetermined types of structures or for structures exhibiting c...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 9; no. 4; pp. 575 - 586
Main Authors Djoko, S., Cook, D.J., Holder, L.B.
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.07.1997
IEEE Computer Society
Subjects
Acceleration
Applied sciences
Artificial intelligence
Circuits
Computer science; control theory; systems
Data analysis
Data compression
Design automation
Design methodology
Exact sciences and technology
Information retrieval. Graph
Information systems. Data bases
Learning and adaptive systems
Memory organisation. Data processing
Process design
Programming
Software
Theoretical computing
Transaction databases
System architecture
Circuit design
Domain knowledge
Data compression
Expert system
Knowledge representation
Knowledge base
Data mining
Algorithm
Computational complexity
Graph
System performance
Coding
Database
Minimum description lenght principle
Inference rule
Computer aided design
Software engineering
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ISSN1041-4347
DOI10.1109/69.617051

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Summary:Discovering repetitive, interesting, and functional substructures in a structural database improves the ability to interpret and compress the data. However, scientists working with a database in their area of expertise often search for predetermined types of structures or for structures exhibiting characteristics specific to the domain. The paper presents a method for guiding the discovery process with domain specific knowledge. The SUBDUE discovery system is used to evaluate the benefits of using domain knowledge to guide the discovery process. Domain knowledge is incorporated into SUBDUE following a single general methodology to guide the discovery process. Results show that domain specific knowledge improves the search for substructures that are useful to the domain and leads to greater compression of the data. To illustrate these benefits, examples and experiments from the computer programming, computer aided design circuit, and artificially generated domains are presented.
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ISSN:1041-4347
DOI:10.1109/69.617051