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MAC: A Multiclass Associative Classification Algorithm

Author

Listed:
  • 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)

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.

Suggested Citation

  • Neda Abdelhamid & Aladdin Ayesh & Fadi Thabtah & Samad Ahmadi & Wael Hadi, 2012. "MAC: A Multiclass Associative Classification Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-10.
  • Handle: RePEc:wsi:jikmxx:v:11:y:2012:i:02:n:s0219649212500116
    DOI: 10.1142/S0219649212500116
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    References listed on IDEAS

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    1. David S. Evans & Albert L. Nichols & Richard Schmalensee, 2005. "U.S. v. Microsoft: Did Consumers Win?," NBER Working Papers 11727, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Hepzi Jeya Pushparani & Nancy Jasmine Goldena, 2022. "Reliable Distributed Fuzzy Discretizer for Associative Classification of Big Data," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-13, January.
    2. Faisal Aburub & Wa’el Hadi, 2018. "A New Associative Classification Algorithm for Predicting Groundwater Locations," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-26, December.

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