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Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

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Abstract

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.

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Authors and Affiliations

  1. Department of Computer Engineering, TOBB ETU Economics and Technology University, Sogutozu Cad No. 43, Sogutozu, 06560, Ankara, Turkey

    Tansel Özyer

  2. Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

    Ming Zhang & Reda Alhajj

  3. Department of Computer Science, Global University, Beirut, Lebanon

    Reda Alhajj

Authors
  1. Tansel Özyer
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  2. Ming Zhang
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  3. Reda Alhajj
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Correspondence to Reda Alhajj.

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Özyer, T., Zhang, M. & Alhajj, R. Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl Intell 35, 110–122 (2011). https://doi.org/10.1007/s10489-009-0206-7

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  • DOI: https://doi.org/10.1007/s10489-009-0206-7

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