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 |