Cluster

Example 1

Summary: Perform hierarchical cluster analysis

Code:

// Open data table
dt = Open("$Sample_Data/Birth Death.jmp");
// Hierarchical Cluster
Hierarchical Cluster(
    Columns( :birth, :death ),
    Color Clusters( 1 ),
    Mark Clusters( 1 ),
    Number of Clusters( 14 )
);

Example 2

Summary: Generate a hierarchical cluster

Code:

// Open data table
dt = Open("$Sample_Data/Crops.jmp");
// Hierarchical Cluster
Hierarchical Cluster(
    Columns( :S1, :S3 ),
    Color Clusters( 1 ),
    Mark Clusters( 1 ),
    Number of Clusters( 7 )
);

Example 3

Summary: Perform a k means cluster analysis with normal mixtures

Code:

// Open data table
dt = Open("$Sample_Data/Cytometry.jmp");
// Normal Mixtures
K Means Cluster(
    Y( :CD3, :CD8, :CD4, :MCB ),
    {Mixtures Tolerance( 0.00000001 ),
    Mixtures MaxIter( 200 ),
    Mixtures N Starts( 20 ),
    Outlier Cluster( 0 ),
    Diagonal Variance( 0 ),
    Number of Clusters( 6 ),
    Normal Mixtures, Go( Biplot 3D( 1 ) )
    },
    SendToReport(
        Dispatch( {}, "Control Panel",
            OutlineBox,
            {Close( 1 )}
        )
    )
);

Example 4

Summary: Perform hierarchical clustering on flight distances using the Ward method with standardized data, and display the dendrogram with 10 clusters colored for easy identification.

Code:

// Open data table
dt = Open("$Sample_Data/Flight Distances.jmp");
// Hierarchical Cluster
Hierarchical Cluster(
    Y(
        :Birmingham, :Boston, :Buffalo,
        :Chicago, :Cleveland, :Dallas,
        :Denver, :Detroit, :El Paso,
        :Houston, :Indianapolis,
        :Kansas City, :Los Angeles,
        :Louisville, :Memphis, :Miami,
        :Minneapolis, :New Orleans,
        :New York, :Omaha, :Philadelphia,
        :Phoenix, :Pittsburgh, :St. Louis,
        :Salt Lake City, :San Francisco,
        :Seattle, :Washington DC
    ),
    Label( :Cities ),
    Method( "Ward" ),
    Standardize Data( 1 ),
    Distance Matrix( 1 ),
    Dendrogram Scale( "Distance Scale" ),
    Number of Clusters( 10 ),
    Color Clusters( 1 ),
    SendToReport(
        Dispatch( {}, "Dendrogram",
            OutlineBox,
            {SetHorizontal( 1 )}
        )
    )
);

Example 5

Summary: Perform hierarchical cluster analysis on factor scores using the Ward method with standardized data and generate cluster summary and two-way clustering with a specified number of clusters and dendrogram scale.

Code:

// Open data table
dt = Open("$Sample_Data/Online Consumer Data.jmp");
// Hierarchical Cluster on Factor Scores
Hierarchical Cluster(
    Y(
        :Privacy, :Security, :Reputation,
        :Trust, :Purchase Int
    ),
    Method( "Ward" ),
    Standardize Data( 1 ),
    Cluster Summary( 1 ),
    Two Way Clustering,
    Number of Clusters( 4 ),
    Dendrogram Scale( "Distance Scale" ),
    More Color Map Columns(
        :Internet Use
    )
);

Example 6

Summary: Perform hierarchical clustering on the Penguin dataset using centroid linkage and standardize the data. Generate a constellation plot.

Code:

// Open data table
dt = Open("$Sample_Data/Penguins.jmp");
// Hierarchical Clustering
Hierarchical Cluster(
    Y(
        :Culmen Length, :Culmen Depth,
        :Flipper Length, :Body Mass,
        :Delta 15 N, :Delta 13 C
    ),
    Label( :Species ),
    Method( "Centroid" ),
    Standardize Data( 1 ),
    Color Clusters( 1 ),
    Dendrogram Scale( "Distance Scale" ),
    Number of Clusters( 4 ),
    Constellation Plot( 1 )
);

Example 7

Summary: Perform hierarchical spatial clustering on defects using the Ward method .

Code:

// Open data table
dt = Open("$Sample_Data/Wafer Stacked.jmp");
// Spatial Cluster of Defects
Hierarchical Cluster(
    Y( :Defects ),
    Object ID( :Lot, :Wafer ),
    Attribute ID( :X_Die, :Y_Die ),
    Method( "Ward" ),
    Standardize Data( 0 ),
    Cluster Summary( 1 ),
    Dendrogram Scale( "Distance Scale" ),
    Number of Clusters( 7 ),
    Add Spatial Measures(
        Attributes( 1 ),
        Angle( 1 ),
        Radius( 1 ),
        Streak Angle( 1 ),
        Streak Distance( 1 )
    ),
    SendToReport(
        Dispatch( {}, "Dendrogram",
            OutlineBox,
            {Close( 1 ),
            SetHorizontal( 1 )}
        ),
        Dispatch( {"Dendrogram"},
            "Clust Dendro", FrameBox,
            {Frame Size( 35, 700 )}
        )
    )
);

Example 8

Summary: Perform hierarchical cluster analysis on crude death rate and crude birth rate using Ward's method and geometric spacing scaling with 14 clusters.

Code:

// Open data table
dt = Open("$Sample_Data/World Demographics.jmp");
// Hierarchical Cluster: Crude Death Rate and Crude Birth Rate
Hierarchical Cluster(
    Y(
        :"Crude Death Rate (1000)"n,
        :"Crude Birth Rate (1000)"n
    ),
    Method( "Ward" ),
    Standardize( 1 ),
    Color Clusters( 1 ),
    Mark Clusters( 1 ),
    Dendrogram Scale(
        "Geometric Spacing"
    ),
    Number of Clusters( 14 )
);