The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
MAFIA: a maximal frequent itemset algorithm for transactional databases
Abstract
We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy combines a vertical bitmap representation of the database with an efficient relative bitmap compression schema. In a thorough experimental analysis of our algorithm on real data, we isolate the effect of the individual components of the algorithm. Our performance numbers show that our algorithm outperforms previous work by a factor of three to five.
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Citations
Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818
Constrained frequent pattern mining: a pattern-growth view
Pei, J., & Han, J. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/568574.568580 · Cited by 133
Metadata
- Authors
- Burdick, D., Calimlim, M., & Gehrke, J.
- Venue
- Proceedings 17th International Conference on Data Engineering
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-13
- Pages
- 443-452
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W2108560469
- Cited by
- 612
- References
- 3
- FWCI
- 31.13
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Burdick, D., Calimlim, M., & Gehrke, J. (2002). MAFIA: a maximal frequent itemset algorithm for transactional databases. Proceedings 17th International Conference on Data Engineering. https://doi.org/10.1109/icde.2001.914857
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