The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Fast algorithms for frequent itemset mining using FP-trees
Abstract
Efficient algorithms for mining frequent itemsets are crucial for mining association rules as well as for many other data mining tasks. Methods for mining frequent itemsets have been implemented using a prefix-tree structure, known as an FP-tree, for storing compressed information about frequent itemsets. Numerous experimental results have demonstrated that these algorithms perform extremely well. In this paper, we present a novel FP-array technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree-based algorithms. Our technique works especially well for sparse data sets. Furthermore, we present new algorithms for mining all, maximal, and closed frequent itemsets. Our algorithms use the FP-tree data structure in combination with the FP-array technique efficiently and incorporate various optimization techniques. We also present experimental results comparing our methods with existing algorithms. The results show that our methods are the fastest for many cases. Even though the algorithms consume much memory when the data sets are sparse, they are still the fastest ones when the minimum support is low. Moreover, they are always among the fastest algorithms and consume less memory than other methods when the data sets are dense.
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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
Fast algorithms for mining association rules
Agrawal, R., & Srikant, R. (1998) · Cited by 11,307
Fast Algorithms for Mining Association Rules in Large Databases
Agrawal, R., & Srikant, R. (1994) · Very Large Data Bases · Cited by 9,419
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
Agrawal, R., & Srikant, R. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380415 · Cited by 5,155
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335372 · Cited by 3,216
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Han, J., Pei, J., Yin, Y., & Mao, R. (2004) · Data Mining and Knowledge Discovery · doi:10.1023/b:dami.0000005258.31418.83 · Cited by 2,629
Scalable algorithms for association mining
Zaki, M. J. (2000) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.846291 · Cited by 1,751
Knuth, D. E. (1973) · Cumulative Index of Computer Aided Architectural Design · Cited by 1,719
An Efficient Algorithm for Mining Association Rules in Large Databases
Savasere, A., Omiecinski, E. R., & Navathe, S. B. (1995) · Cited by 1,590
Discovery of Frequent Episodes in Event Sequences
Mannila, H., Toivonen, H., & Inkeri Verkamo, A. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009748302351 · Cited by 1,463
Discovering Frequent Closed Itemsets for Association Rules
Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49257-7_25 · Cited by 1,378
Efficiently mining long patterns from databases
Bayardo, R. J. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276313 · Cited by 1,302
Sampling Large Databases for Association Rules
Toivonen, H. T. T. (1996) · Cited by 1,089
CHARM: An Efficient Algorithm for Closed Itemset Mining
Zaki, M. J., & Hsiao, C.-J. (2002) · Proceedings of the 2002 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972726.27 · Cited by 929
Levelwise Search and Borders of Theories in Knowledge Discovery
Mannila, H., & Toivonen, H. T. T. (1997) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009796218281 · Cited by 889
CLOSET : An Efficient Algorithm for Mining Frequent Closed Itemsets
Pei, J. (2000) · Cited by 824
Efficient algorithms for mining closed itemsets and their lattice structure
Zaki, M. J., & Hsiao, C.-J. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.60 · Cited by 681
Efficient algorithms for discovering association rules
Mannila, H., Toivonen, H. T. T., & Verkamo, A. I. (1994) · Cited by 624
MAFIA: a maximal frequent itemset algorithm for transactional databases
Burdick, D., Calimlim, M., & Gehrke, J. (2002) · Proceedings 17th International Conference on Data Engineering · doi:10.1109/icde.2001.914857 · Cited by 612
Fast vertical mining using diffsets
Zaki, M. J., & Gouda, K. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956788 · Cited by 593
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Wang, J., Han, J., & Pei, J. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956779 · Cited by 585
Using a hash-based method with transaction trimming for mining association rules
Park, J. S., Chen, M.-S., & Yu, P. S. (1997) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.634757 · Cited by 440
Efficiently mining long patterns from databases
Bayardo, R. J. (1998) · ACM SIGMOD Record · doi:10.1145/276305.276313 · Cited by 398
Efficiently mining maximal frequent itemsets
Gouda, K., & Zaki, M. J. (2002) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989514 · Cited by 397
EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT
Borgelt, C. (2003) · Cited by 394
Mining top-k frequent closed patterns without minimum support
Han, J., Wang, J., Lu, Y., & Tzvetkov, P. (2003) · 2002 IEEE International Conference on Data Mining, 2002. Proceedings. · doi:10.1109/icdm.2002.1183905 · Cited by 252
Metarule-Guided Mining of Multi-Dimensional Association RulesUsing Data Cubes
Kamber, M., Han, J., & Chiang, J. Y. (1997) · Cited by 209
Xin, D., Han, J., Li, X., & Wah, B. W.-S. (2003) · Elsevier eBooks · doi:10.1016/b978-012722442-8/50049-5 · Cited by 134
AFOPT: An Efficient Implementation of Pattern Growth Approach.
Liu, G., Lü, H., Yu, J. X., Wang, W., & Xiao, X. (2003) · Cited by 100
Mining Frequent Itemsets using Patricia Tries.
Pietracaprina, A., & Zandolin, D. (2003) · Cited by 77
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets.
Orlando, S., Lucchese, C., Palmerini, P., Perego, R., & Silvestri, F. (2003) · Cited by 71
Mining Frequent Itemsets from Secondary Memory
Grahne, G., & Zhu, J. (2005) · Proceedings · doi:10.1109/icdm.2004.10116 · Cited by 48
Self-citationSmartMiner: a depth first algorithm guided by tail information for mining maximal frequent itemsets
Zou, Q., Chu, W. W., & Lu, B. (2003) · Proceedings · doi:10.1109/icdm.2002.1184003 · Cited by 47
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A distributed frequent itemset mining algorithm using Spark for Big Data analytics
Zhang, F., Liu, M., Gui, F., Shen, W., Shami, A., & Ma, Y. (2015) · Cluster Computing · doi:10.1007/s10586-015-0477-1 · Cited by 87
Lin, W., Ma, M., Pan, D., & Wang, P. (2018) · 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) · doi:10.1109/pccc.2018.8711092 · Cited by 29
Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures
Koukaras, P., & Tjortjis, C. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9100258 · Cited by 3
Metadata
- Authors
- Grahne, G., & Zhu, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2005-10
- Volume
- 17
- Issue
- 10
- Pages
- 1347-1362
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2151953639
- Cited by
- 549
- References
- 34
- FWCI
- 48.36
- Type
- journal article
- Language
- en
- Open access
- closed
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