MAC: A Multiclass Associative Classification Algorithm

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dc.contributor.author Abdelhamid, Neda en
dc.contributor.author Ayesh, Aladdin, 1972- en
dc.contributor.author Thabtah, Fadi en
dc.contributor.author Ahmadi, Samad en
dc.contributor.author Hadi, Wael en
dc.date.accessioned 2012-08-14T11:23:06Z
dc.date.available 2012-08-14T11:23:06Z
dc.date.issued 2012-06
dc.identifier.citation Abdelhamid, N., Ayesh, A., Thabtah, F. et al (2012), MAC: A Multiclass Associative Classification Algorithm. Journal of Information and Knowledge Management, 11 (2), pp. 1250011-1 - 1250011-10 en
dc.identifier.issn 0219-6492
dc.identifier.uri http://hdl.handle.net/2086/6827
dc.description.abstract Associative classification (AC) is a data mining approach that uses association rule discovery methods to build classification systems (classifiers). Several research studies reveal that AC normally generates higher accurate classifiers than classic classification data mining approaches such as rule induction, probabilistic and decision trees. This paper proposes a new multiclass AC algorithm called MAC. The proposed algorithm employs a novel method for building the classifier that normally reduces the resulting classifier size in order to enable end-user to more understand and maintain it. Experimentations against 19 different data sets from the UCI data repository and using different common AC and traditional learning approaches have been conducted with reference to classification accuracy and the number of rules derived. The results show that the proposed algorithm is able to derive higher predictive classifiers than rule induction (RIPPER) and decision tree (C4.5) algorithms and very competitive to a known AC algorithm named MCAR. Furthermore, MAC is also able to produce less number of rules than MCAR in normal circumstances (standard support and confidence thresholds) and in sever circumstances (low support and confidence thresholds) and for most of the data sets considered in the experiments. en
dc.language.iso en en
dc.subject associative classification en
dc.subject associative rule en
dc.subject data mining en
dc.subject rule learning en
dc.title MAC: A Multiclass Associative Classification Algorithm en
dc.type Article en
dc.identifier.doi http://dx.doi.org/10.1142/S0219649212500116
dc.ref2014.selected 1367395509_9810681032646_11_4


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