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  "Description": "Algorithms of distance-based k-medoids clustering: simple\nand fast k-medoids, ranked k-medoids, and increasing number of\nclusters in k-medoids. Calculate distances for mixed variable\ndata such as Gower, Podani, Wishart, Huang, Harikumar-PV, and\nAhmad-Dey. Cluster validation applies internal and relative\ncriteria. The internal criteria includes silhouette index and\nshadow values. The relative criterium applies bootstrap\nprocedure producing a heatmap with a flexible reordering matrix\nalgorithm such as complete, ward, or average linkages. The\ncluster result can be plotted in a marked barplot or pca\nbiplot.",
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      "title": "kmed: Distance-Based K-Medoids",
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        "2. Distance Computation",
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        "2.A.2. squared Euclidean weighted by range (method = \"ser\")",
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        "2.C.3 Podani (method = \"podani\")",
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        "3.C. Rank k-medoids algorithm (rankkmed)",
        "3.D. Increasing number of clusters k-medoids algorithm (inckmed)",
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