MAC: A Multiclass Associative Classification Algorithm

Neda Abdelhamid (), Aladdin Ayesh (), Fadi Thabtah (), Samad Ahmadi () and Wael Hadi ()
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Neda Abdelhamid: Informatics Department, De Montfort University, Leicester, LE1 9BH, UK
Aladdin Ayesh: Informatics Department, De Montfort University, Leicester, LE1 9BH, UK
Fadi Thabtah: Managment Information System Department, Philadelphia University, Amman, Jordan
Samad Ahmadi: Informatics Department, De Montfort University, Leicester, LE1 9BH, UK
Wael Hadi: Managment Information System Department, Philadelphia University, Amman, Jordan

Journal of Information & Knowledge Management (JIKM), 2012, vol. 11, issue 02, 1-10

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.

Keywords: Associative classification; associative rule; data mining; rule learning (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219649212500116

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