Abstract
In this paper, we introduced a new data structure called DISG (Directed itemsets graph)in which the information of frequent itemsets was stored. Based on it, a new algorithm called DBDG(DFS Based -DISG) was developed by using depth first searching strategy. At last we performed a experiment on a real dataset to test the run time of DBDG. The experiment showd that it was efficient for mining dense datasets.
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
Agrawal R., Imielinski T., Swami A., “Mining association rules between sets of items in very large databases.” Proceedings of the ACM SIGMOD Conference on Management of data, washington, USA, (1993) 207–216
Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In Proc. of the 20th Int’l Conference on Very Large Databases, Santiago, Chile, (1994)
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. Proc. 1995 Int. Conf. Very Large Data Bases (VLDB’95), Zurich, Switzerland, (1995) 432–443
J. S. Park, M. S. Chen, and P. S. Yu. An efficient hash-based algorithm for mining association rules. Proc. 1995 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD’95), San Jose, CA, (1995)175–186
Brin S., Motwani R. Ullman J. D. and Tsur S. Dynamic Itemset Counting and implication rules for Market Basket Data. Proceedings of the ACM SIGMOD, (1997)255–264
J. Han, J. Pei and Y. Yin. Mining Frequent Patterns without Candidate Generation. Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data (SIGMOD00), Dallas, TX, USA, (2000) 1–12
Agarwal R. C., Aggarwal C. C., Prasad V. V. V., Crestana V., A Tree Projection Algorithm for Generation of Large Itemsets For Association Rules. Journal of Parallel and Distributed Computing, Special Issue on High Performance Data Mining, 61(3), (2001)350–371
R. C. Agarwal, C. C. Aggarwal, and V.V.V. Prasad. Depth first generation of long patterns. In Proc. of the 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, USA, (2000)108–118
Roberto J. Bayardo. Efficiently mining long patterns from databases. In Proceedings of ACM-SIGMOD International Conference on Management of Data, (1998) 85–93
N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal. Discovering frequent closed itemsets for association rules. Proc. 7th Int. Conf. Database Theory (ICDT99), Jerusalem, Israel, (1999) 398–416
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wen, L., Li, M. (2003). A New Association Rules Mining Algorithms Based on Directed Itemsets Graph. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_111
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_111
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive