Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

The Algorithm for Mining Global Frequent Itemsets based on Big Data

Authors
Bo He
Corresponding Author
Bo He
Available Online July 2015.
DOI
https://doi.org/10.2991/lemcs-15.2015.31How to use a DOI?
Keywords
Data mining; Global frequent itemsets; Big data; Mapreduce; FP-tree
Abstract
There were some algorithms for mining global frequent itemsets. Most of them adopted apriori-like algorithm, so that a lot of candidate itemsets were generated. To solve the problems, the algorithm for mining global frequent itemsets based on big data was proposed, namely, MGFI algorithm. MGFI algorithm computed local frequent itemsets by mapreduce, then the center node collected data, finally, global frequent itemsets were got by mapreduce. MGFI algorithm required less communication traffic by the searching strategies of top-down and bottom-up. Theoretical analysis and experimental results suggest that MGFI algorithm is fast and effective.
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Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-6252-102-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-15.2015.31How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Bo He
PY  - 2015/07
DA  - 2015/07
TI  - The Algorithm for Mining Global Frequent Itemsets based on Big Data
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.31
DO  - https://doi.org/10.2991/lemcs-15.2015.31
ID  - He2015/07
ER  -