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Supported distance measures include Dynamic Time Warping (DTW) and Euclidean Distance (ED).\n\n## Installation\n\nInstall the released version from CRAN:\n```r\ninstall.packages(\"WOAkMedoids\")\n```\n\nOr install the development version from GitHub:\n```r\ndevtools::install_github(\"WOAkMedoids/woakmedoids\")\n```\n\n## Usage\n```r\nlibrary(WOAkMedoids)\n\ndata(Lightning7)\nLightning7_data \u003c- Lightning7[, -1]  # Remove the first column of classification data\n\nresult \u003c- woa_kmedoids(Lightning7_data, ClusNum = 7, distance_method = \"dtw\", learned_w = 5)\nprint(result)\n```\n\n## Reference\n\nChenan H. and Tsutsumida N. (2025) A scalable k-medoids clustering via whale optimization algorithm, *Array*, 28, 100599. [https://doi.org/10.1016/j.array.2025.100599](https://doi.org/10.1016/j.array.2025.100599)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoakmedoids%2Fwoakmedoids","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwoakmedoids%2Fwoakmedoids","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwoakmedoids%2Fwoakmedoids/lists"}