Abstract
In contrast to mining over transactional data, graph mining is done over structured data represented in the form of a graph. Data having structural relationships lends itself to graph mining. Subdue is one of the early main memory graph mining algorithms that detects the best substructure that compresses a graph using the minimum description length principle. Database approach to graph mining presented in this paper overcomes the problems – performance and scalability – inherent to main memory algorithms. The focus of this paper is the development of graph mining algorithms (specifically Subdue) using SQL and stored procedures in a Relational database environment. We have not only shown how the Subdue class of algorithms can be translated to SQL-based algorithms, but also demonstrated that scalability can be achieved without sacrificing performance.
This work was supported, in part, by the Office of Naval Research, the SPAWAR System Center-San Diego & by the Rome Laboratory (grant F30602-02-2-0134), and by NSF grant IIS-0097517
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© 2004 Springer-Verlag Berlin Heidelberg
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Chakravarthy, S., Beera, R., Balachandran, R. (2004). DB-Subdue: Database Approach to Graph Mining. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_42
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DOI: https://doi.org/10.1007/978-3-540-24775-3_42
Publisher Name: Springer, Berlin, Heidelberg
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