Association Analysis of Large Sample Data Based on Hadoop

DOI

10.2991/iiicec-15.2015.277How to use a DOI?

Keywords

Hadoop; Mahout; Association Rule Mining; FP-growth Algorithm; Pattern Assessment

Abstract

This paper implemented effective associate rule mining based on Hadoop parallel computing. First, the parallel FP-growth algorithm was run on Hadoop platform to find the frequent item sets of the transaction data. Second, the strong association rules was generated from the frequent item sets by a designed algorithm. Then, redundant rules were deleted according to filtering conditions to make model evaluation. After those steps, all the funny and non-redundancy strong association rules were mined out. In addition, this paper also analyzed the efficiency of the Hadoop parallel computing and explained the superiority of the Hadoop parallel computing when it handles big data.

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  - Ran An
AU  - Jingchang Pan
PY  - 2015/03
DA  - 2015/03
TI  - Association Analysis of Large Sample Data Based on Hadoop
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 1255
EP  - 1258
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.277
DO  - 10.2991/iiicec-15.2015.277
ID  - An2015/03
ER  -