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Significant Association Rule Mining Without Support and Confidence Thresholds

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Data Intelligence and Cognitive Informatics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

User-defined specification of support and confidence thresholds often degrades the significance of the association rules. The task is highly domain-dependent and henceforth leads to discovery of huge number of association rules. In addition, the existing approaches exclude rare itemsets for significant association rules. Significance depends on the associability between the itemsets forming association rules. This paper introduces an efficient technique for discovering significant association rules with high associability using multiple minimum supports (MMS), jaccard similarity index, and maximum permissible flexible dissociation. Subsequently, an efficient algorithm called SAMSAR (Semi-Automated Mining of Significant Association Rule) is developed that does not require support and confidence thresholds. Performance of the proposed SAMSAR is quite satisfactory in comparison with the relevant approaches.

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Author information

Authors and Affiliations

  1. Department of CSE, University of Kalyani, Kalyani, West Bengal, India

    Subrata Datta & Kalyani Mali

Authors
  1. Subrata Datta
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  2. Kalyani Mali
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, GITAM University, Bangalore, India

    I. Jeena Jacob

  2. Assistant Professor of Computer Science, Department of Mathematics and Computer Science, Ashland University, Ashland, OH, USA

    Selvanayaki Kolandapalayam Shanmugam

  3. Department of Telecommunication Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Robert Bestak

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Datta, S., Mali, K. (2022). Significant Association Rule Mining Without Support and Confidence Thresholds. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6460-1_17

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