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  "Title": "Methods for Clustering Mixed-Type Data",
  "Description": "Implements methods for clustering mixed-type data,\nspecifically combinations of continuous and nominal data.\nSpecial attention is paid to the often-overlooked problem of\nequitably balancing the contribution of the continuous and\ncategorical variables. This package implements KAMILA\nclustering, a novel method for clustering mixed-type data in\nthe spirit of k-means clustering. It does not require dummy\ncoding of variables, and is efficient enough to scale to rather\nlarge data sets. Also implemented is Modha-Spangler clustering,\nwhich uses a brute-force strategy to maximize the cluster\nseparation simultaneously in the continuous and categorical\nvariables. For more information, see Foss, Markatou, Ray, &\nHeching (2016) <doi:10.1007/s10994-016-5575-7> and Foss &\nMarkatou (2018) <doi:10.18637/jss.v083.i13>.",
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