The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
Abstract
For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has its own advantage and disadvantage with respect to the density of the input database. In this paper, we propose an efficient way to combine these three data structures so that in any case the combination gives the best performance.
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Cites
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
Cited by
A classification for community discovery methods in complex networks
Coscia, M., Giannotti, F., & Pedreschi, D. (2011) · Statistical Analysis and Data Mining: The ASA Data Science Journal · doi:10.1002/sam.10133 · Cited by 353
Frequent itemset mining: A 25 years review
Luna, J. M., Fournier‐Viger, P., & Ventura, S. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1329 · Cited by 258
Metadata
- Authors
- Uno, T., Kiyomi, M., & Arimura, H.
- Venue
- Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
- Publisher
- ACM
- Published
- 2005-08-21
- Pages
- 77-86
- OpenAlex
- W2009380894
- Cited by
- 233
- References
- 1
- FWCI
- 7.91
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Uno, T., Kiyomi, M., & Arimura, H. (2005). LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining. Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations. https://doi.org/10.1145/1133905.1133916
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