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  "Title": "Kernel Distance Metric Learning for Mixed-Type Data",
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  "Description": "Distance metrics for mixed-type data consisting of\ncontinuous, nominal, and ordinal variables. This methodology\nuses additive and product kernels to calculate similarity\nfunctions and metrics, and selects variables relevant to the\nunderlying distance through bandwidth selection via maximum\nsimilarity cross-validation. These methods can be used in any\ndistance-based algorithm, such as distance-based clustering.\nFor further details, we refer the reader to Ghashti and\nThompson (2024) <doi:10.1007/s00357-024-09493-z> for dkps()\nmethodology, and Ghashti (2024) <doi:10.14288/1.0443975> for\ndkss() methodology.",
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        "~metriclearning"
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      "title": "Distance using Kernel Summation Similarity (DKSS) for Mixed-type Data",
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        "~metrics",
        "~metriclearning"
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        "kdml"
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