HeMI ++: a genetic algorithm based clustering technique for sensible clusters

Citació

  • Beg AH, Islam Z, Estivill-Castro V. HeMI ++: a genetic algorithm based clustering technique for sensible clusters. In: 2020 IEEE Congress on Evolutionary Computation (CEC); 2020 Jul 19-24; Glasgow, UK. New Jersey: IEEE; 2020. p.495-504. DOI: 10.1109/CEC48606.2020.9185882

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Descripció

  • Resum

    We propose a new clustering technique called HeMI++. It uses cleansing and cloning operations that help to produce sensible clusters. HeMI++ learns necessary properties of a good clustering solution for a dataset from a high-quality initial population, without requiring any user input. It then disqualifies the chromosomes that do not satisfy the properties through its cleansing operation. In the cloning operation, HeMI++ replaces the chromosomes by high-quality chromosomes already found in the initial population. We compare HeMI++ with six (6) existing techniques on twenty (20) publicly available datasets using the Tree Index metric. Our experimental results indicate a clear superiority of HeMI++ over existing methods. We also apply HeMI++ on a brain dataset and demonstrate its ability to produce sensible clusters.
  • Descripció

    Comunicació presentada al IEEE Congress on Evolutionary Computation (CEC 2020), celebrat del 19 al 24 de juliol de 2020 a Glasgow, Escòcia.
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