• Siti Sakira Kamaruddin School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
  • Yuhanis Yusof School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
  • Husniza Husni School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia
  • Mohammad Hayel Al Refai School of Computing, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia




Text mining, Frequent Pattern Mining, Associative Classification, Multi Class Association Rule.


This paper presents text classification using a modified Multi Class Association Rule Method. The method is based on Associative Classification which combines classification with association rule discovery. Although previous work proved that Associative Classification produces better classification accuracy compared to typical classifiers, the study on applying Associative Classification to solve text classification problem are limited due to the common problem of high dimensionality of text data and this will consequently results in exponential number of generated classification rules. To overcome this problem the modified Multi-Class Association Rule Method was enhanced in two stages. In stage one the frequent pattern are represented using a proposed vertical data format to reduce the text dimensionality problem and in stage two the generated rule was pruned using a proposed Partial Rule Match to reduce the number of generated rules. The proposed method was tested on a text classification problem and the result shows that it performed better than the existing method in terms of classification accuracy and number of generated rules.


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How to Cite

TEXT CLASSIFICATION USING MODIFIED MULTI CLASS ASSOCIATION RULE. (2016). Jurnal Teknologi, 78(8-2). https://doi.org/10.11113/jt.v78.9553