Abstract
Data mining in knowledge discovery helps people discover unknown patterns from the collected data. PIETM (Principle of Inclusion–Exclusion and Transaction Mapping) algorithmis a novel frequent item sets mining algorithm, which scans database twice. To cope with big transaction database in the cloud, this paper proposes a method that parallelizes PIETM by the MapReduce framework. The method has three modules. Module I counts the supports of frequent 1-item sets. Module II constructs transaction interval lists. Module III discovers all the frequent item sets iteratively.
This research is supported in part by NSC in Taiwan under Grant No. NSC-101-2221-E-025-014.
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© 2013 Springer Science+Business Media Dordrecht
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Chen, SY., Li, JH., Lin, KC., Chen, HM., Chen, TS. (2013). Using MapReduce Framework for Mining Association Rules. In: Park, J.J., Barolli, L., Xhafa, F., Jeong, H.Y. (eds) Information Technology Convergence. Lecture Notes in Electrical Engineering, vol 253. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6996-0_76
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DOI: https://doi.org/10.1007/978-94-007-6996-0_76
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