Abstract
Building a system from disparate software requires analysis to establish commonality of code. The ability of a data mining tool to extract repeating functional structures is the first step to reduce exploration, save development time, and re-use software components. This case study looks specifically at the application of graph-based data mining algorithms to code re-factoring. After writing a module to obtain a graph representation of a discrete event model, we built a tool around the University of Washington’s SUBDUE package to find recurring patterns of logic. This resulted in cleaner code and increased awareness of code re-use.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Holder, L.B., Ketkar, N.S., Cook, D.J.: Subdue: Compression-Based Frequent Pattern Discovery in Graph Data. In: Proceedings of the ACM KDD Workshop on Open-Source Data Mining (2005)
Grünewald, P.: A Tutorial on the Minimum Description Length Principle, from Advances in Minimum Description Length: Theory and Applications. MIT Press, Cambridge (2004)
Kotonya, G., Sommerville, I.: Requirements Engineering: Processes and Techniques. John Wiley and Sons, New York (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Holland, D.A. (2008). Using Data Mining to Build Integrated Discrete Event Simulations. In: Perner, P. (eds) Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects. ICDM 2008. Lecture Notes in Computer Science(), vol 5077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70720-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-70720-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70717-2
Online ISBN: 978-3-540-70720-2
eBook Packages: Computer ScienceComputer Science (R0)