Abstract
Data mining extracts implicit, previously unknown and potentially useful information from databases. Many approaches have been proposed to extract information, and one of the most important ones is finding frequent patterns in databases. Although much work has been done to this problem, to the best of our knowledge, no previous research studies how to find frequent DAG (directed acyclic graph) patterns from DAG data. Without such a mining method, the knowledge cannot be discovered from the databases storing DAG data such as family genealogy profiles, product structures, XML documents and course structures. Therefore, a solution method containing four stages is proposed in this paper to discover frequent DAG patterns from DAG databases.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Lin, X., Liu, C., Zhang, Y., Zhou, X.: Efficiently computing frequent tree-like topology patterns in a web environment. In: Proceedings of Technology of Object-Oriented Languages and Systems (1999)
Wang, K., Liu, H.Q.: Discovering structural association of semistructured data. IEEE Trans. on Knowledge and Data Engineering 12, 353–371 (2000)
Wang, K., Liu, H.Q.: Mining is-part-of association patterns from semistructured data. In: Proceedings of the 9th IFIP 2.6 Working Conference on Database Semantics (2001)
Inokuuchi, A., Washio, T., Motoda, H.: An Apriori-based Algorithm for mining frequent substructures from graph data. In: Proceeding of the 4th European Conference on Principles and Practices of Knowledge Discovery in Databases, pp. 13–23 (2000)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of the IEEE International Conference on Data Mining (2001)
Nanopoulos, A., Manolopoulos, Y.: Mining patterns from graph traversals. Data and Knowledge Engineering 37, 243–266 (2001)
Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: Proceedings of 2002 Int. Conf. on Data Mining (ICDM 2002), Maebashi, Japan (2002)
Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In: Proceedings of 2003 ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD 2003), Washington, D.C. (2003)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 478–499 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YL., Kao, HP., Ko, MT. (2004). Mining DAG Patterns from DAG Databases. In: Li, Q., Wang, G., Feng, L. (eds) Advances in Web-Age Information Management. WAIM 2004. Lecture Notes in Computer Science, vol 3129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27772-9_58
Download citation
DOI: https://doi.org/10.1007/978-3-540-27772-9_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22418-1
Online ISBN: 978-3-540-27772-9
eBook Packages: Springer Book Archive