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An Empirical Study of Domain Knowledge and Its Benefits to Substructure Discovery
July-August 1997 (vol. 9 no. 4)
pp. 575-586

Abstract—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. This paper presents a method for guiding the discovery process with domain-specific knowledge. In this paper, 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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Index Terms:
Data mining, minimum description length principle, data compression, inexact graph match, domain knowledge.
Citation:
Surnjani Djoko, Diane J. Cook, Lawrence B. Holder, "An Empirical Study of Domain Knowledge and Its Benefits to Substructure Discovery," IEEE Transactions on Knowledge and Data Engineering, vol. 9, no. 4, pp. 575-586, July-Aug. 1997, doi:10.1109/69.617051
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