The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Frequent Itemset Mining for Big Data
Abstract
Frequent Itemset Mining (FIM) is one of the most well known techniques to extract knowledge from data. The combinatorial explosion of FIM methods become even more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming already provide good tools to tackle this problem. However, these tools come with their own technical challenges, e.g. balanced data distribution and inter-communication costs. In this paper, we investigate the applicability of FIM techniques on the MapReduce platform. We introduce two new methods for mining large datasets: Dist-Eclat focuses on speed while BigFIM is optimized to run on really large datasets. In our experiments we show the scalability of our methods.
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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
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
- Moens, S., Aksehirli, E., & Goethals, B.
- Venue
- 2013 IEEE International Conference on Big Data
- Publisher
- IEEE
- Published
- 2013-10
- Pages
- 111-118
- OpenAlex
- W2079931838
- Cited by
- 213
- References
- 1
- FWCI
- 16.14
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Moens, S., Aksehirli, E., & Goethals, B. (2013). Frequent Itemset Mining for Big Data. 2013 IEEE International Conference on Big Data. https://doi.org/10.1109/bigdata.2013.6691742
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