Paper Info
Title
Mr-Arm: A Map-Reduce Association Rule Mining Framework
Abstract
Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, we overcome the problem of mining very large data by proposing a new parallel Map-Reduce (MR) association rule mining technique called MR-ARM that uses a hybrid data transformation format to quickly finding frequent items and generating rules. The MR programming paradigm is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use, and therefore the proposed algorithm develops a fast parallel distributed batch set intersection method for finding frequent items. Two implementations (Weka, Hadoop) of the proposed MR association rule algorithm have been developed and a number of experiments against small, medium and large data collections have been conducted. The ground bases of the comparisons are time required by the algorithm for: data initialisation, frequent items discovery, rule generation, etc. The results show that MR-ARM is very useful tool for mining association rules from large datasets in a distributed environment.
Year
DOI
Venue
2013
10.1142/S0129626413500126
PARALLEL PROCESSING LETTERS
Keywords
Field
DocType
Association Rules Mining, Data Mining, Distributed tasks, Hadoop, MapReduce, Parallel processes
Intersection (set theory),Data mining,Programming paradigm,Computer science,Usability,Apriori algorithm,Implementation,Association rule learning,Database transaction,K-optimal pattern discovery
Journal
Volume
Issue
ISSN
23
3
0129-6264
Citations 
PageRank 
References 
3
0.45
4
Authors
2
Name
Order
Citations
PageRank
Fadi A. Thabtah153832.28
Suhel Hammoud21307.82