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Soft Set and Genetic Algorithms for Association Rule Mining: A Road Map and Direction for Hybridization

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Book cover Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 199))

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Abstract

Association rules have relied on user-specified threshold of support and confidence. With no prior/little domain knowledge, if the user is specifying threshold for the mining task; then there is a direct impact on quality of association rules. In this paper, we have discussed some of the early attempts of choosing automatically the user specified threshold (i.e., no user intervention to specify threshold) by soft set and genetic algorithms for association rule mining.

The reason of being restricted with soft set and genetic algorithms is that: association rule using soft set is free from inadequacy of the parameterization tools, which can also deals with uncertainty. Alongside, genetic algorithms can help to user for finding out optimal threshold for generating a number of interesting and novel association rules. Furthermore, we discuss the possibility of hybridization and their future usage in association rule mining.

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Authors and Affiliations

  1. School of Computer Application, KIIT University, Patia, Bhubaneswar, Odisha, India, 751006

    Satya Ranjan Dash

  2. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, Odisha, India, 756019

    Satchidananda Dehuri

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  1. Satya Ranjan Dash
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  2. Satchidananda Dehuri
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Correspondence to Satya Ranjan Dash .

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Editors and Affiliations

  1. Dept of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. AI Lab, University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  3. Bhubaneswar Engineering College, Bhubaneswar, India

    Bhabendra Narayan Biswal

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Dash, S.R., Dehuri, S. (2013). Soft Set and Genetic Algorithms for Association Rule Mining: A Road Map and Direction for Hybridization. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_44

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  • DOI: https://doi.org/10.1007/978-3-642-35314-7_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35313-0

  • Online ISBN: 978-3-642-35314-7

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