Abstract
Maximal Frequent Patterns can be mined using breadth-first search or depth-first search. The pure BFS algorithms work well when all maximal frequent itemsets are short. The pure DFS algorithms work well when all maximal frequent itemsets are long. Both the pure BFS and pure DFS techniques will not be efficient, when the dataset contains some of long maximal frequent itemsets and some of short maximal frequent itemsets. Efficient pruning techniques are required to mine MFI from these kinds of datasets. An algorithm (MFIMiner) using Breadth-First search with efficient pruning mechanism that competently mines both long and short maximal frequent itemsets is proposed in this paper. The performance of the algorithm is evaluated and compared with GenMax and Mafia algorithms for T40I10D100K, T10I4D100K, and Retail dataset. The result shows that the proposed algorithm has significant improvement than existing algorithms for sparse datasets.
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
References
Bayardo, R.: Efficiently mining long patterns from databases. In ACM SIGMOD Conference (1998)
Lin, D., Kedem, Z.M.: Pincer-Search: a new algorithm for discovering the maximum frequent set. In: Proceedings of VI International Conference on Extending Database Technology (1998)
Agrawal, R., Aggarwal, C., Prasad, V.: Depth first generation of long patterns. In: 7th International Conference on Knowledge Discovery and Data Mining, pp. 108–118 (2000)
Burdick, D., Calimlim, M., Gehrke, J.: MAFIA: a maximal frequent itemset algorithm for transactional databases. In: International Conference on Data Engineering, pp: 443–452, April 2001, doi: 10.1.1.100.6805
Gouda, K., Zaki, M.J. (2005). GenMax: an efficient algorithm for mining maximal frequent itemsets. Proc. Data Min. Knowl. Discov, 11, 1–20 (2005)
Agrawal, R., Imielienski, T., Swami, A.: Mining association rules between sets of items in largedatabases. In: Bunemann, P., Jajodia, S. (eds.) Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, pp. 207–216. ACM Press, Newyork (1993)
http://sci-hub.tw/http://www.aaai.org/Papers/KDD/1997/KDD97-060.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sumathi, K., Kannan, S., Nagarajan, K. (2019). Maximal Frequent Itemset Mining Using Breadth-First Search with Efficient Pruning. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_31
Download citation
DOI: https://doi.org/10.1007/978-981-10-8681-6_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8680-9
Online ISBN: 978-981-10-8681-6
eBook Packages: EngineeringEngineering (R0)