The Algorithm for Mining Global Frequent Itemsets based on Big Data
Available Online July 2015.
- https://doi.org/10.2991/lemcs-15.2015.31How to use a DOI?
- Data mining; Global frequent itemsets; Big data; Mapreduce; FP-tree
- 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.
- 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 SP - 158 EP - 161 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 -