Abstract
Mining frequent item sets (frequent patterns) in transaction databases is a well known problem in data mining research. This work proposes a sampling-based method to find frequent patterns. The proposed method contains three phases. In the first phase, we draw a small sample of data to estimate the set of frequent patterns, denoted as F S. The second phase computes the actual supports of the patterns in F S as well as identifies a subset of patterns in F S that need to be further examined in the next phase. Finally, the third phase explores this set and finds all missing frequent patterns. The empirical results show that our algorithm is efficient, about two or three times faster than the well-known FP-growth algorithm.
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
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, pp. 478–499 (1994)
Park, J.S., Chen, M.S., Yu, P.S.: Using a hash-based method with transaction trimming for mining association rules. IEEE Transactions on Knowledge and Data Engineering 9, 813–825 (1997)
Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM-SIGMOD Conf. on Management of Data, pp. 255–264 (1997)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proceedings of Int’l Conf. Very Large Data Bases, pp. 432–444 (1995)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of SIGMOD, pp. 1–12 (2000)
Relue, R., Wu, X., Huang, H.: Efficient runtime generation of association rules. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 466–473 (2001)
Liu, J., Pan, Y., Wang, K., Han, J.: Mining frequent item sets by opportunistic projection. In: Proceedings of 2002 Int. Conf. on Knowledge Discovery in Databases, pp. 229–238 (2002)
Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings of IEEE International Conference on Data Mining, pp. 441–448 (2001)
Agrawal, R.C., Aggarwal, C.C., Prasad, V.V.V.: Depth first generation of long patterns. In: Proceedings of SIGKDD Conference, pp. 108–118 (2000)
Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of 17th Int. Conf. Data Engineering, pp. 443–452 (2001)
Toivonen, H.: Sampling large databases for association rules. In: Proceedings of the 22th International Conference on Very Large Databases, pp. 134–145 (1996)
Agarwal, R., Aggarwal, C., Prasad, V.V.V.: A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing 61, 350–371 (2001)
Bayardo Jr., R.J.: Efficiently mining long patterns from databases. In: Proceedings of the ACM-SIGMOD Int’l Conf. on Management of Data, pp. 85–93 (1998)
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
Chen, YL., Ho, CY. (2005). A Sampling-Based Method for Mining Frequent Patterns from Databases. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_65
Download citation
DOI: https://doi.org/10.1007/11540007_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
eBook Packages: Computer ScienceComputer Science (R0)