Algorithm for Map/Reduce-based association rules data mining

DOI

10.2991/isca-13.2013.56How to use a DOI?

Keywords

Apriori algorithm; Map/Reduce; parallel processing matting

Abstract

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.

Copyright

© 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/).

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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  -