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DOI: 10.14569/IJACSA.2022.0130523
PDF

Improved ISODATA Clustering Method with Parameter Estimation based on Genetic Algorithm

Author 1: Kohei Arai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
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Abstract: Improved ISODATA clustering method with merge and split parameters as well as initial cluster center determination with GA: Genetic Algorithm is proposed. Although ISODATA method is well-known clustering method, there is a problem that the iteration and clustering result is strongly depending on the initial parameters, especially the threshold for merge and split. Furthermore, it shows a relatively poor clustering performance in the case that the probability density function of data in concern cannot be expressed with convex function. To overcome this situation, GA is introduced for the determination of initial cluster center as well as the threshold of merge and split between constructing clusters. Through experiments with simulated data, the well-known the University of California, Irvine: UCI repository data for clustering performance evaluations and ASTER/VNIR: Advanced Spaceborne Thermal Emission and Reflection Radiometer / Visible and Near Infrared Radiometer onboard Terra satellite of imagery data, the proposed method is confirmed to be superior to the conventional ISODATA method.

Keywords: ISODATA clustering; nonlinear merge and split; concaveness of probability density function: PDF; remote sensing satellite imagery data; clustering; genetic algorithm: GA; nonlinear optimization

Kohei Arai, “Improved ISODATA Clustering Method with Parameter Estimation based on Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130523

@article{Arai2022,
title = {Improved ISODATA Clustering Method with Parameter Estimation based on Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130523},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130523},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Kohei Arai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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