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A New Classification Based on Association Algorithm

Author

Listed:
  • Fadi Thabtah

    (MIS Department, Philadelphia University, Jordan)

  • Qazafi Mahmood

    (Computing Department, Hubbersfield University, UK)

  • Lee McCluskey

    (Computing Department, Hubbersfield University, UK)

  • Hussein Abdel-Jaber

    (Computing Department, The World Islamic Sciences & Education University, Jordan)

Abstract

Associative classification is a branch in data mining that employs association rule discovery methods in classification problems. In this paper, we introduce a novel data mining method called Looking at the Class (LC), which can be utilised in associative classification approach. Unlike known algorithms in associative classification such as Classification based on Association rule (CBA), which combine disjoint itemsets regardless of their class labels in the training phase, our method joins only itemsets with similar class labels. This saves too many unnecessary itemsets combining during the learning step, and consequently results in massive saving in computational time and memory. Moreover, a new prediction method that utilises multiple rules to make the prediction decision is also developed in this paper. The experimental results on different UCI datasets reveal that LC algorithm outperformed CBA with respect to classification accuracy, memory usage, and execution time on most datasets we consider.

Suggested Citation

  • Fadi Thabtah & Qazafi Mahmood & Lee McCluskey & Hussein Abdel-Jaber, 2010. "A New Classification Based on Association Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 55-64.
    • Handle: RePEc:wsi:jikmxx:v:09:y:2010:i:01:n:s0219649210002486
    DOI: 10.1142/S0219649210002486
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    References listed on IDEAS

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    1. Mayes, Rick, 2006. "Chronic Politics: Health Care Security from FDR to George W. Bush. By Philip J. Funigiello. Lawrence: University of Kansas Press, 2006. xii + 395 pp. Index, notes, bibliography. Cloth, $39.95. ISBN: 0," Business History Review, Cambridge University Press, vol. 80(3), pages 587-589, October.
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    Cited by:

    1. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    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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