RCuA: rule classification use association data mining model for structure and unstructured data

  • Authors

    • Mohammed Hayel Refai Sur University College
    • Saleh Ali Alomari Jadara University
    • Tarik Khalil Sur University College
    • Hussam Saleh Abu Karaki Al Hussein Bin Talal University
    2019-04-07
    https://doi.org/10.14419/ijet.v7i4.28379
  • Text Categorization, Associative Classification, CBA, MCAR, RCuA, UCI, Reuters-21578.

  • Association and classification rule mining are important activities in the data mining domain. Incorporating the association rule discovery and classification within this domain leads to a method, called the associative classification method. Text Categorizations (TC) prevails form major problems through this domain including machine learning communities. This issue is not simple to be solved since available data has enormous dimensionality. There exist particular enormous amounts of online documents within a group of data in which each data is combined along with a particular class. Categorization refers to a structure of design from a categorized data, which categorizes past unrecognized documents as accurate as it could be. The paper proposes a novel text classification model by applying an Associative Classification (AC) model, namely, the Rule Classification use Association (RCuA), which produces an obvious text document. Additionally, the paper attempts at forming an expansion of available AC of current associative text classifiers, which cope with structure and unstructured English document assemblies. The produced model is tested through two experiments of structure and unstructured data. The first experiment is related to the UCI datasets, while the second is related to Reuters-21578 datasets. The experiment is based on utilizing various classification categorization learning algorithms (e.g. MCAR and CBA) in order to assess the efficiency of the proposed model in this paper. As a result, it is found to be proven from the findings that the new RCuA model improves the accuracy of the dataset in comparison with the MCAR and CBA algorithms where the number of existing rules is decreased. The RCuA makes an average accuracy of 83.945% compared to the CBA and MCAR algorithms resulting with an accuracy of 82.34% and 83.655%, respectively. In terms of unstructured dataset, the RCuA produces an average accuracy of 89.328% in comparison with the CBA and MCAR algorithms resulting with an accuracy of 77.34% and 83.64286%, respectively.

     

     

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    Hayel Refai, M., Ali Alomari, S., Khalil, T., & Saleh Abu Karaki, H. (2019). RCuA: rule classification use association data mining model for structure and unstructured data. International Journal of Engineering & Technology, 7(4), 5659-5665. https://doi.org/10.14419/ijet.v7i4.28379