Algorithm for Map/Reduce-based association rules data mining
- 10.2991/isca-13.2013.56How to use a DOI?
- Apriori algorithm; Map/Reduce; parallel processing matting
In order to realize massive information data mining, the traditional Apriori algorithm is updated into a Map/Reduce-based frequent itemsets generating method, so as to distribute the massive data into several servers for parallel processing. The construction of Hadoop platform helps to realize this method which is also compared with Apriori algorithm. The experimental results show that, in the process to generate frequent itemsets of massive data, this method can make full use of the advantages of parallel processing, followed by better timeliness.
- © 2013, 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/).
Cite this article
TY - CONF AU - Wenqi Wang AU - Qiang Li PY - 2013/10 DA - 2013/10 TI - Algorithm for Map/Reduce-based association rules data mining BT - Proceedings of 2013 International Conference on Information Science and Computer Applications PB - Atlantis Press SP - 334 EP - 339 SN - 1951-6851 UR - https://doi.org/10.2991/isca-13.2013.56 DO - 10.2991/isca-13.2013.56 ID - Wang2013/10 ER -