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Apriori-based frequent itemset mining algorithms on MapReduce
Abstract
Many parallelization techniques have been proposed to enhance the performance of the Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce functions, MapReduce has emerged and excels in the mining of datasets of terabyte scale or larger in either homogeneous or heterogeneous clusters. Minimizing the scheduling overhead of each map-reduce phase and maximizing the utilization of nodes in each phase are keys to successful MapReduce implementations. In this paper, we propose three algorithms, named SPC, FPC, and DPC, to investigate effective implementations of the Apriori algorithm in the MapReduce framework. DPC features in dynamically combining candidates of various lengths and outperforms both the straight-forward algorithm SPC and the fixed passes combined counting algorithm FPC. Extensive experimental results also show that all the three algorithms scale up linearly with respect to dataset sizes and cluster sizes.
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Citations
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
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
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
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
Metadata
- Authors
- Lin, M.-Y., Lee, P.-Y., & Hsueh, S.-C.
- Venue
- Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
- Publisher
- ACM
- Published
- 2012-02-20
- Pages
- 1-8
- OpenAlex
- W1995797177
- Cited by
- 233
- References
- 1
- FWCI
- 14.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lin, M.-Y., Lee, P.-Y., & Hsueh, S.-C. (2012). Apriori-based frequent itemset mining algorithms on MapReduce. Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication. https://doi.org/10.1145/2184751.2184842
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