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  "Title": "Distance Density Clustering Algorithm",
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  "Description": "A distance density clustering (DDC) algorithm in R. DDC\nuses dynamic time warping (DTW) to compute a similarity matrix,\nbased on which cluster centers and cluster assignments are\nfound. DDC inherits dynamic time warping (DTW) arguments and\nconstraints. The cluster centers are centroid points that are\ncalculated using the DTW Barycenter Averaging (DBA) algorithm.\nThe clustering process is divisive. At each iteration, cluster\ncenters are updated and data is reassigned to cluster centers.\nEarly stopping is possible. The output includes cluster centers\nand clustering assignment, as described in the paper (Ma et al\n(2017) <doi:10.1109/ICDMW.2017.11>).",
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  "Author": "Ruizhe Ma [cre, aut], Bing Jiang [aut]",
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