Parallel associative classification data mining frameworks based mapreduce

dc.contributor.author Thabtah, Fadi
dc.contributor.author Hammoud, Suhel
dc.contributor.author Abdel-Jaber, Hussein
dc.date.accessioned 2020-02-13T09:13:02Z
dc.date.available 2020-02-13T09:13:02Z
dc.date.copyright 2015 en_US
dc.date.issued 2015-06
dc.description This article is not available at CUD collection. The version of scholarly record of this Article is published in Parallel Processing Letters (2015), available online at: https://doi.org/10.1142/S0129626415500024. en_US
dc.description.abstract Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy. © 2015 World Scientific Publishing Company. en_US
dc.identifier.citation Thabtah, F., Hammoud, S., & Abdel-Jaber, H. (2015). Parallel associative classification data mining frameworks based mapreduce. Parallel Processing Letters, 25(2). https://doi.org/10.1142/S0129626415500024 en_US
dc.identifier.issn 01296264
dc.identifier.uri http://dx.doi.org/10.1142/S0129626415500024
dc.identifier.uri http://hdl.handle.net/20.500.12519/139
dc.language.iso en en_US
dc.publisher World Scientific Publishing Co. Pte Ltd en_US
dc.relation Authors 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; Abdel-Jaber, H., Faculty of Computer Studies, Arab Open University, Saudi Arabia
dc.relation Authors 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; Abdel-Jaber, H., Faculty of Computer Studies, Arab Open University, Saudi Arabia
dc.relation.ispartofseries Parallel Processing Letters;Vol. 25, no. 2
dc.rights Permission to reuse the abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights Permission to reuse the abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holder Copyroght : 2015 World Scientific Publishing Company
dc.rights.holder Copyright : 2015 World Scientific Publishing Company
dc.subject Algorithms en_US
dc.subject Association rules en_US
dc.subject Classification (of information) en_US
dc.subject Learning algorithms en_US
dc.subject Learning systems en_US
dc.subject Associative classification en_US
dc.subject Distributed tasks en_US
dc.subject Hadoop en_US
dc.subject Map-reduce en_US
dc.subject Parallel minings en_US
dc.subject Data mining en_US
dc.title Parallel associative classification data mining frameworks based mapreduce en_US
dc.type Article en_US
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