International Journal of Networked and Distributed Computing

Volume 1, Issue 2, April 2013, Pages 89 - 96

Parallel Implementation of Apriori Algorithm Based on MapReduce

Authors
Ning Li, Li Zeng, Qing He, Zhongzhi Shi
Corresponding Author
Ning Li
Available Online 15 January 2013.
DOI
https://doi.org/10.2991/ijndc.2013.1.2.3How to use a DOI?
Keywords
Apriori algorithm; Frequent itemsets; MapReduce; Parallel implementation; Large database.
Abstract
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware.
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Journal
International Journal of Networked and Distributed Computing
Volume-Issue
1 - 2
Pages
89 - 96
Publication Date
2013/01
ISSN
2211-7946
DOI
https://doi.org/10.2991/ijndc.2013.1.2.3How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Ning Li
AU  - Li Zeng
AU  - Qing He
AU  - Zhongzhi Shi
PY  - 2013
DA  - 2013/01
TI  - Parallel Implementation of Apriori Algorithm Based on MapReduce
JO  - International Journal of Networked and Distributed Computing
SP  - 89
EP  - 96
VL  - 1
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2013.1.2.3
DO  - https://doi.org/10.2991/ijndc.2013.1.2.3
ID  - Li2013
ER  -