The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficiently mining long patterns from databases
Abstract
We present a pattern-mining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data show that when the patterns are long, our algorithm is more efficient by an order of magnitude or more.
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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
Cited by
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
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
Efficient mining of partial periodic patterns in time series database
Han, J., Dong, G., & Yin, Y. (1999) · Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337) · doi:10.1109/icde.1999.754913 · Cited by 587
Metadata
- Authors
- Bayardo, R. J.
- Venue
- Proceedings of the 1998 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 1998-06
- Pages
- 85-93
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2141115288
- Cited by
- 1,302
- References
- 1
- FWCI
- 30.32
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proceedings of the 1998 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/276304.276313
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
