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International Conference on Machine Learning and Data Mining in Pattern Recognition

MLDM 2016: Machine Learning and Data Mining in Pattern Recognition pp 435–445Cite as

On Genetic Algorithms for Detecting Frequent Item Sets And Large Bite Sets

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9729))

Abstract

This paper introduces the use of genetic algorithms to mine binary datasets for obtaining frequent item sets and large bite item sets, two classes of problems that are important for optimal exposure of item sets to customers and for efficient advertising campaigns. Whereas both problems can be approached in a common framework, we highlight specific features of the fitness functions suitable for each of the problems.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Massachusetts Boston, Boston, USA

    Roman A. Sizov & Dan A. Simovici

Authors
  1. Roman A. Sizov
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  2. Dan A. Simovici
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Corresponding author

Correspondence to Dan A. Simovici .

Editor information

Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Sizov, R.A., Simovici, D.A. (2016). On Genetic Algorithms for Detecting Frequent Item Sets And Large Bite Sets. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2016. Lecture Notes in Computer Science(), vol 9729. Springer, Cham. https://doi.org/10.1007/978-3-319-41920-6_34

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  • DOI: https://doi.org/10.1007/978-3-319-41920-6_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41919-0

  • Online ISBN: 978-3-319-41920-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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