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Research Article | Open Access |

Fisher Distance Based GA Clustering Taking Into Account Overlapped Space Among Probability Density Functions of Clusters in Feature Space

Author 1: Kohei Arai
International Journal of Advanced Research in Artificial Intelligence (IJARAI) · Vol. 2, No. 11 · Published 2013

DOI: https://doi.org/10.14569/IJARAI.2013.021105

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Abstract

Fisher distance based Genetic Algorithm: GA clustering method which takes into account overlapped space among probability density functions of clusters in feature space is proposed. Through experiments with simulation data of 2D and 3D feature space generated by random number generator, it is found that clustering performance depends on overlapped space among probability density function of clusters. Also it is found relation between cluster performance and the GA parameters, crossover and mutation probability as well as the number of features and the number of clusters.

Keywords

GA clustering Fisher distance crossover mutation overlapped space among probability density functions of clusters

How to Cite this Article

Arai, K. (2013). Fisher Distance Based GA Clustering Taking Into Account Overlapped Space Among Probability Density Functions of Clusters in Feature Space. International Journal of Advanced Research in Artificial Intelligence, 2(11). https://doi.org/10.14569/IJARAI.2013.021105

Arai, Kohei. "Fisher Distance Based GA Clustering Taking Into Account Overlapped Space Among Probability Density Functions of Clusters in Feature Space." International Journal of Advanced Research in Artificial Intelligence, vol. 2, no. 11, 2013, https://doi.org/10.14569/IJARAI.2013.021105.

@article{Arai2013,
  title     = {Fisher Distance Based GA Clustering Taking Into Account Overlapped Space Among Probability Density Functions of Clusters in Feature Space},
  journal   = {International Journal of Advanced Research in Artificial Intelligence},
  volume    = {2},
  number    = {11},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Kohei Arai},
  doi       = {10.14569/IJARAI.2013.021105},
  url       = {https://doi.org/10.14569/IJARAI.2013.021105}
}

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