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A Study of Interestingness Measures for Knowledge Discovery in Databases—A Genetic Approach

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Book cover Computational Intelligence in Data Mining - Volume 2

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 32))

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Abstract

One of the vital areas of attention in the field of knowledge discovery is to analyze the interestingness measures in rule discovery and to select the best one according to the situation. There is a wide variety of interestingness measures available in data mining literature and it is difficult for user to select appropriate measure in a particular application domain. The main contribution of the paper is to compare these interestingness measures on diverse datasets by using genetic algorithm and select the best one according to the situation.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India

    Goyal Garima & Jyoti Vashishtha

Authors
  1. Goyal Garima
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  2. Jyoti Vashishtha
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Corresponding author

Correspondence to Goyal Garima .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Garima, G., Vashishtha, J. (2015). A Study of Interestingness Measures for Knowledge Discovery in Databases—A Genetic Approach. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_8

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_8

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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