The Algorithm for Mining Global Frequent Itemsets based on Big Data

DOI

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo He
PY  - 2015/07
DA  - 2015/07
TI  - The Algorithm for Mining Global Frequent Itemsets based on Big Data
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 158
EP  - 161
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.31
DO  - 10.2991/lemcs-15.2015.31
ID  - He2015/07
ER  -