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Significant Association Rule Mining Without Support and Confidence Thresholds

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

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.

Keywords

  • Association rule mining
  • Associability
  • Multiple minimum supports
  • Jaccard similarity
  • Permissible flexible dissociation

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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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