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  "Title": "Clustering Large Datasets by Merging K-Means Solutions",
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  "Description": "Fast clustering of large datasets by hierarchically\nmerging components of a K-means solution based on the pairwise\noverlap between the Gaussian mixture components implied by the\nK-means partition, as proposed by Melnykov and Michael (2020)\n<doi:10.1007/s00357-019-09314-8>. Implements the DEMP-K merging\nalgorithm with single, Ward's, average, and complete linkages,\nthe overlap map display for selecting the number of clusters,\nfour K-means variants corresponding to Gaussian mixtures with\nspherical or elliptical, homoscedastic or heteroscedastic\ncomponents, and a tool for selecting the number of K-means\ncomponents.",
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      "title": "Choose the number of K-means components",
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      "title": "Clustering by merging K-means solutions (DEMP-K)",
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      "title": "Broom tidiers for MergeKmeans fits",
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        "Two half-circular clusters",
        "The overlap map",
        "An inscribed cluster",
        "Choosing K and predicting new data",
        "Compact overlapping clusters",
        "Beyond classical K-means",
        "Reference"
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