Volume 1, Issue 2, April 2013, Pages 89 - 96
Parallel Implementation of Apriori Algorithm Based on MapReduce
Ning Li, Li Zeng, Qing He, Zhongzhi Shi
Received 22 March 2012, Accepted 13 November 2012, Available Online 1 April 2013.
- https://doi.org/10.2991/ijndc.2013.1.2.3How to use a DOI?
- Apriori algorithm; Frequent itemsets; MapReduce; Parallel implementation; Large database.
- 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.
- 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 - 2013/04 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 -