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Browsing School of Management by Author "Abdelhamid, Neda"
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Item Associative classification approaches : review and comparison(World Scientific Publishing Co. Pte Ltd, 2014) Abdelhamid, Neda; Thabtah, FadiAssociative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions. © 2014 World Scientific Publishing Co.Item Associative classification common research challenges(Institute of Electrical and Electronics Engineers Inc., 2016) Abdelhamid, Neda; Jabbar, Ahmad Abdul; Thabtah, FadiAssociation rule mining involves discovering concealed correlations among variables often from sales transactions to help managers in key business decision involving items shelving, sales and planning. In the last decade, association rule mining methods have been employed in deriving rules from classification dataset in different business domains. This has resulted in an emergence of new classification approach called Associative Classification (AC), which often produces higher predictive classifiers than classic approaches such as decision trees, greedy and rule induction. Nevertheless, AC suffers from noticeable challenges some of which have been inherited from association rules and others have been resulted from building the classifier phase. These challenges are not limited to the massive numbers of candidate ruleitems found, the very large classifiers derived, the inability to handle multi-label datasets, and the design of rule pruning, ranking and prediction procedures. This article highlights and critically analyzes common challenges faced by AC algorithms that are still sustained. Hence, it opens the door for interested researchers to further investigate these challenges hoping to enhance the overall performance of this approach and increase it applicability in research domains. © 2016 IEEE.Item Phishing detection based associative classification data mining(Elsevier Ltd, 2014-10-01) Abdelhamid, Neda; Ayesh, Aladdin; Thabtah, Fadi