Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference

Association Analysis of Large Sample Data Based on Hadoop

Authors
Ran An, Jingchang Pan
Corresponding Author
Ran An
Available Online March 2015.
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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Volume Title
Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
Series
Advances in Computer Science Research
Publication Date
March 2015
ISBN
978-94-62520-54-7
ISSN
2352-538X
DOI
10.2991/iiicec-15.2015.277How to use a DOI?
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/).

Cite this article

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  -