Mining Predicate Rules without Minimum Support Threshold


  • Hafiz Ishfaq Ahmad Universiti Teknologi Malaysia
  • Alex Tze Hiang Sim Universiti Teknologi Malaysia
  • Syed Mazhar Ali Shah Universiti Teknologi Malaysia
  • Mohammad Abrar Bacha Khan University
  • Asma Gul Shaheed Benazir Bhutto Women University Peshawar



association rule mining, coherent rules, inference rules, interestingness measure, minimum support threshold, predicate logic.


Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules supportlessly using predicate logic and a property of a proposed interestingness measure ( measure). The algorithm scans the database and uses  measure’s property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule’s consequence. 

Author Biographies

Hafiz Ishfaq Ahmad, Universiti Teknologi Malaysia

School of Computing, Faculty of Engineering

Alex Tze Hiang Sim, Universiti Teknologi Malaysia

Senior Lecturer,

School of Computing, Faculty of Engineering

Syed Mazhar Ali Shah, Universiti Teknologi Malaysia

School of Computing, Faculty of Engineering

Mohammad Abrar, Bacha Khan University

Assistant Professor, 

Department of Computer Science

Asma Gul, Shaheed Benazir Bhutto Women University Peshawar

Assistant Professor,

Department of Statistics


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