The MapReduce Model on Cascading Platform for Frequent Itemset Mining

https://doi.org/10.22146/ijccs.34102

Nur Rokhman(1*), Amelia Nursanti(2)

(1) Department of Electronics and Computer Science, FMIPA UGM, Yogyakarta
(2) Computer Science Study Program FMIPA UGM
(*) Corresponding Author

Abstract


The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.

Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data.

This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).


Keywords


Frequent Itemset Mining; MapReduce; Cascading

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References

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DOI: https://doi.org/10.22146/ijccs.34102

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