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Associative Classification Approaches: Review and Comparison

Author

Listed:
  • Neda Abdelhamid

    (Computing and Informatics Department, De Montfort University, Leicester, UK)

  • Fadi Thabtah

    (Ebusiness Department, Canadian University of Dubai, Dubai, UAE)

Abstract

Associative 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.

Suggested Citation

  • Neda Abdelhamid & Fadi Thabtah, 2014. "Associative Classification Approaches: Review and Comparison," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-30.
    • Handle: RePEc:wsi:jikmxx:v:13:y:2014:i:03:n:s0219649214500270
    DOI: 10.1142/S0219649214500270
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    Citations

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    Cited by:

    1. Chanchala Hathurusingha & Neda Abdelhamid & David Airehrour, 2019. "Forecasting Models Based on Data Analytics for Predicting Rice Price Volatility: A Case Study of the Sri Lankan Rice Market," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-20, March.
    2. Majed Rajab, 2019. "Visualisation Model Based on Phishing Features," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-17, March.
    3. Fadi Thabtah & Firuz Kamalov, 2017. "Phishing Detection: A Case Analysis on Classifiers with Rules Using Machine Learning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1-16, December.
    4. Said Baadel & Joan Lu, 2019. "Data Analytics: Intelligent Anti-Phishing Techniques Based on Machine Learning," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 1-17, March.

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