Mr-arm : a map-reduce association rule mining framework

dc.contributor.authorThabtah, Fadi
dc.contributor.authorHammoud, Suhel
dc.date.accessioned2020-02-13T09:19:06Z
dc.date.available2020-02-13T09:19:06Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this Article is published in Parallel Processing Letters (2013), available online at: https://doi.org/10.1142/S0129626413500126en_US
dc.description.abstractAssociation 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. © 2013 World Scientific Publishing Company.en_US
dc.identifier.citationThabtah, F., & Hammoud, S. (2013). Mr-arm: A map-reduce association rule mining framework. Parallel Processing Letters, 23(3). https://doi.org/10.1142/S0129626413500126en_US
dc.identifier.issn01296264
dc.identifier.urihttp://dx.doi.org/10.1142/S0129626413500126
dc.identifier.urihttp://hdl.handle.net/20.500.12519/142
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.relationAuthors Affiliations: Thabtah, F., E-Business Dept, Canadian University of Dubai, Dubai, United Arab Emirates; Hammoud, S., Electronic and Computing Dept, Brunel University, Uxbridge, United Kingdom
dc.relation.ispartofseriesParallel Processing Letters;Vol. 23, no. 3
dc.rightsPermission to reuse abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holderCopyright : 2013 World Scientific Publishing Company
dc.subjectAssociation rules miningen_US
dc.subjectDistributed tasksen_US
dc.subjectHadoopen_US
dc.subjectMap-reduceen_US
dc.subjectParallel processen_US
dc.subjectAlgorithmsen_US
dc.subjectAssociation rulesen_US
dc.subjectMultiprocessing systemsen_US
dc.subjectData miningen_US
dc.titleMr-arm : a map-reduce association rule mining frameworken_US
dc.typeArticleen_US

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