Abstract
Currently, constraints are increasingly considered as a kind of means of user- or expert-control for filtering those unsatisfied and redundant patterns rapidly during the web mining process. Recent work has highlighted the importance of constraint-based mining paradigm in the context of frequent itemsets, sequences, and many other interesting patterns in large database. However, it is still not clear how to push various constraints systematically into graph mining process. In this paper, we categorize various graph-based constraints into several major classes and develop a framework CabGin (i.e. Constraint-based Graph Mining) to push them into mining process by their categories. Non-monotonic aggregates like average also can be pushed into CabGin with minor revision. Experimental results show that CabGin can prunes a large search space effectively by pushing graph-based constraints into mining process.
This research is supported in part by the Key Program of National Natural Science Foundation of China (No. 69933010 and 60303008), China National 863 High-Tech Projects (No. 2002AA4Z3430 and 2002AA231041).
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
Garofalakis, M., Rastogi, R., Shim, K.: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints. In: Proc. 1999 Int. Conf. Very Large Database (VLDB 1999), Edinburgh, UK (September 1999)
Huan, J., Wang, W., Prins, J.: Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism. In: Proc. 2003 Int. Conf. Data Mining (ICDM 2003), Melbourne, USA (December 2003)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data. In: Proc. 2000 European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD 2000), Lyon, France (September 2000)
Kuramochi, M., Karypis, G.: Frequent Subgraph Discovery. In: Proc. 2001 Int. Conf. Data Mining (ICDM 2001), San Jose, Canada (November 2001)
Ng, R., Lakshmanan, L., Han, J., Pang, A.: Exploratory Mining and Pruning Optimizations of Constrained Associations Rules. In: Proc. 1998 ACM Int. Conf. Management of Data (SIGMOD 1998), Seattle, USA (June 1998)
Pei, J., Han, J.: Can We Push More Constraints into Frequent Pattern Mining? In: Proc. 2000 ACM Int. Conf. Knowledge Discovery and Data Mining (KDD 2000), Boston, USA (August 2000)
Pei, J., Han, J., Lakshmanan, L.: Mining Frequent Itemsets with Convertible Constraints. In: Proc. 2001 IEEE Int. Conf. Data Engineering (ICDE 2001), Heidelberg, Germany (April 2001)
Pei, J., Han, J., Wang, W.: Mining Sequential Patterns with Constraints in Large Databases. In: Proc. 2002 ACM Int. Conf. Information and Knowledge Management (CIKM 2002), McLean, USA (November 2002)
Srikant, R., Vu, Q., Agrawal, R.: Mining Association Rules with Item Constraints. In: Proc. 1997 ACM Int. Conf. Knowledge Discovery and Data Mining (KDD 1997), Newport Beach, Canada (August 1997)
Wang, C., Wang, W., Pei, J., Zhu, Y., Shi, B.: Scalable Mining of Large Disk-Based Graph Database. In: Proc. 2004 ACM Int. Conf. Knowledge Discovery and Data Mining (KDD 2004), Seattle, USA (August 2004)
Yan, X., Han, J.: gSpan: Graph-based Substructure Pattern Mining. In: Proc. 2002 Int. Conf. Data Mining (ICDM 2002), Maebashi City, Japan (December 2002)
Yan, X., Han, J.: CloseGraph: Mining Closed Frequent Graph Patterns. In: Proc. 2003 ACM Int. Conf. Knowledge Discovery and Data Mining (KDD 2003), Washington, USA (July 2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, C., Zhu, Y., Wu, T., Wang, W., Shi, B. (2005). Constraint-Based Graph Mining in Large Database. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds) Web Technologies Research and Development - APWeb 2005. APWeb 2005. Lecture Notes in Computer Science, vol 3399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31849-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-31849-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25207-8
Online ISBN: 978-3-540-31849-1
eBook Packages: Computer ScienceComputer Science (R0)