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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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      "topics": [
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      "title": "Simple k-medoid algorithm",
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      "title": "kmed: Distance-Based K-Medoids",
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        "2. Distance Computation",
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        "2.A.1. Manhattan weighted by range (method = \"mrw\")",
        "2.A.2. squared Euclidean weighted by range (method = \"ser\")",
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        "2.A.4. squared Euclidean weighted by variance (method = \"sev\")",
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        "2.C.3 Podani (method = \"podani\")",
        "2.C.4 Huang (method = \"huang\")",
        "2.C.5 Harikumar and PV (method = \"harikumar\")",
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        "3.C. Rank k-medoids algorithm (rankkmed)",
        "3.D. Increasing number of clusters k-medoids algorithm (inckmed)",
        "3.E. Simple k-medoids algorithm (skm)",
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        "5. Cluster visualization",
        "A. Biplot",
        "B. Marked barplot",
        "References"
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