K Means Cluster

Associated Constructors

K Means Cluster

Syntax: K Means Cluster( Y( column(s) ), Number of Clusters( number ) )

Description: Clusters rows based on numeric variables in data tables with up to millions of rows. You must specify the number of clusters in advance.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;

Columns

By

Syntax: obj = K Means Cluster(...<By( column(s) )>...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;

Freq

Syntax: obj = K Means Cluster(...<Freq( column )>...)

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Quality Control/Failure3Freq.jmp" );
obj = K Means Cluster(
    Y(
        :contamination, :corrosion, :doping, :metallization, :miscellaneous, :oxide defect,
        :silicon defect
    ),
    Freq( :SampleSize ),
    Number of Clusters( 2 ),
    Go
);

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Quality Control/Failure3Freq.jmp" );
obj = Normal Mixtures(
    Y(
        :contamination, :corrosion, :doping, :metallization, :miscellaneous, :oxide defect,
        :silicon defect
    ),
    Freq( :SampleSize ),
    Number of Clusters( 2 ),
    Go
);

Weight

Syntax: obj = K Means Cluster(...<Weight( column )>...)

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Quality Control/Failure3Freq.jmp" );
obj = K Means Cluster(
    Y(
        :contamination, :corrosion, :doping, :metallization, :miscellaneous, :oxide defect,
        :silicon defect
    ),
    Weight( :SampleSize ),
    Number of Clusters( 2 ),
    Go
);

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Quality Control/Failure3Freq.jmp" );
obj = Normal Mixtures(
    Y(
        :contamination, :corrosion, :doping, :metallization, :miscellaneous, :oxide defect,
        :silicon defect
    ),
    Weight( :SampleSize ),
    Number of Clusters( 2 ),
    Go
);

Y

Syntax: obj = K Means Cluster(...Y( column(s) )...)

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);

Item Messages

Columns Scaled Individually

Syntax: Columns Scaled Individually( state=0|1 )

Description: Scales each column independently of the other columns. On by default.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Columns Scaled Individually( 1 ),
    Go
);

Go

Syntax: obj << Go

Description: Launches the platform by completing the iterations.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;

Initial Clusters

Syntax: obj << Initial Clusters( "Default" | "Randomize" | column )

Description: Determines how the initial clusters are created. The initial clusters can be randomized or you can specify a categorical column to define the initial clusters, seeding by the means for each category.

JMP Version Added: 19

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Initial Clusters( :Species ),
    Go
);

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Initial Clusters( "Randomize" ),
    Go
);

Max Iterations

Syntax: obj << Max Iterations( number )

Description: Sets the maximum number of iterations.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster( Y( :Sepal length, :Sepal width, :Petal length, :Petal width ) );
obj << Max Iterations( 100 );
obj << Go;

Number of Clusters

Syntax: obj << Number of Clusters( number )

Description: Changes the number of clusters.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
Wait( 1 );
obj << Number of Clusters( 5 );
obj << Go;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
Wait( 1 );
obj << Number of Clusters( 5 );
obj << Go;

SOM

Syntax: obj << SOM

Description: Creates clusters using self-organizing maps. The grid structure used by this method can be used to interpret the clusters in two dimensions.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << SOM;
obj << Go;

SOM Bandwidth

Syntax: obj << SOM Bandwidth( number )

Description: Specifies the bandwidth for the self-organizing maps.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << SOM;
obj << SOM Bandwidth( 0.5 );
obj << Go;

SOM N Rows

Syntax: obj << SOM N Rows( number )

Description: Sets the number of rows for the self-organizing maps.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << SOM( 1 );
obj << SOM N Rows( 3 );
obj << Go;

Shift distances by rates

Syntax: obj << Shift distances by rates( state=0|1 )

Description: Gives higher preference to point assignments to larger clusters.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Shift distances by rates( 1 ),
    Go
);

Single Step

Syntax: obj << Single Step( state=0|1 )

Description: Enables stepping through individual iterations. In the K Means outline, click the Step button for each iteration.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Single Step( 1 );
obj << Go;
obj << Step;
Wait( 1 );
obj << Step;

Use within cluster std dev

Syntax: obj << Use within cluster std dev( state=0|1 )

Description: Calculates distances scaled by the standard deviation estimated for each cluster.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Use within cluster std dev( 1 ),
    Go
);

Shared Item Messages

Action

Syntax: obj << Action

Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Apply Preset

Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )

Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.

JMP Version Added: 18

Anonymous preset

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
dt2 = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj2 = dt2 << Oneway( Y( :LogHist0 ), X( :drug ) );
Wait( 1 );
obj2 << Apply Preset( preset );

Search by name

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "Compare Distributions" );

Search within folder(s)

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "t-Tests", Folder( "Compare Means" ) );

Automatic Recalc

Syntax: obj << Automatic Recalc( state=0|1 )

Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );

Broadcast

Syntax: obj << Broadcast(message)

Description: Broadcasts a message to a platform. If return results from individual objects are tables, they are concatenated if possible, and the final format is identical to either the result from the Save Combined Table option in a Table Box or the result from the Concatenate option using a Source column. Other than those, results are stored in a list and returned.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Quality Control/Diameter.jmp" );
objs = Control Chart Builder(
    Variables( Subgroup( :DAY ), Y( :DIAMETER ) ),
    By( :OPERATOR )
);
objs[1] << Broadcast( Save Summaries );

Column Switcher

Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)

Description: Adds a control panel for changing the platform's variables

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);

Copy ByGroup Script

Syntax: obj << Copy ByGroup Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Copy ByGroup Script;

Copy Script

Syntax: obj << Copy Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Copy Script;

Data Table Window

Syntax: obj << Data Table Window

Description: Move the data table window for this analysis to the front.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Data Table Window;

Get By Levels

Syntax: obj << Get By Levels

Description: Returns an associative array mapping the by group columns to their values.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;

Get ByGroup Script

Syntax: obj << Get ByGroup Script

Description: Creates a script (JSL) to produce this analysis and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
t = obj[1] << Get ByGroup Script;
Show( t );

Get Container

Syntax: obj << Get Container

Description: Returns a reference to the container box that holds the content for the object.

General

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );

Platform with Filter

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
gb = Graph Builder(
    Show Control Panel( 0 ),
    Variables( X( :height ), Y( :weight ) ),
    Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
    Local Data Filter(
        Add Filter(
            columns( :age, :sex, :height ),
            Where( :age == {12, 13, 14} ),
            Where( :sex == "F" ),
            Where( :height >= 55 ),
            Display( :age, N Items( 6 ) )
        )
    )
);
New Window( "platform boxes",
    H List Box(
        Outline Box( "Report(platform)", Report( gb ) << Get Picture ),
        Outline Box( "platform << Get Container", (gb << Get Container) << Get Picture )
    )
);

Get Data Table

Syntax: obj << Get Data Table

Description: Returns a reference to the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
t = obj << Get Datatable;
Show( N Rows( t ) );

Get Group Platform

Syntax: obj << Get Group Platform

Description: Return the Group Platform object if this platform is part of a Group. Otherwise, returns Empty().

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Y( :weight ), X( :height ), By( :sex ) );
group = biv[1] << Get Group Platform;
Wait( 1 );
group << Layout( "Arrange in Tabs" );

Get Script

Syntax: obj << Get Script

Description: Creates a script (JSL) to produce this analysis and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
t = obj << Get Script;
Show( t );

Get Script With Data Table

Syntax: obj << Get Script With Data Table

Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
t = obj << Get Timing;
Show( t );

Get Web Support

Syntax: obj << Get Web Support

Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
s = obj << Get Web Support();
Show( s );

Get Where Expr

Syntax: obj << Get Where Expr

Description: Returns the Where expression for the data subset, if the platform was launched with By() or Where(). Otherwise, returns Empty()

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv2 = dt << Bivariate( X( :height ), Y( :weight ), Where( :age < 14 & :height > 60 ) );
Show( biv[1] << Get Where Expr, biv2 << Get Where Expr );

Ignore Platform Preferences

Syntax: Ignore Platform Preferences( state=0|1 )

Description: Ignores the current settings of the platform's preferences. The message is ignored when sent to the platform after creation.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Ignore Platform Preferences( 1 ),
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

Local Data Filter

Syntax: obj << Local Data Filter

Description: To filter data to specific groups or ranges, but local to this platform

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);

New JSL Preset

Syntax: New JSL Preset( preset )

Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
preset = obj << New JSL Preset( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) );
Wait( 1 );
obj << Apply Preset( preset );

New Preset

Syntax: obj = New Preset()

Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();

Paste Local Data Filter

Syntax: obj << Paste Local Data Filter

Description: Apply the local data filter from the clipboard to the current report.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
filter = dist << Local Data Filter(
    Add Filter( columns( :Region ), Where( :Region == "MW" ) )
);
filter << Copy Local Data Filter;
dist2 = Distribution( Continuous Distribution( Column( :Lead ) ) );
Wait( 1 );
dist2 << Paste Local Data Filter;

Redo Analysis

Syntax: obj << Redo Analysis

Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Redo Analysis;

Redo ByGroup Analysis

Syntax: obj << Redo ByGroup Analysis

Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Redo ByGroup Analysis;

Relaunch Analysis

Syntax: obj << Relaunch Analysis

Description: Opens the platform launch window and recalls the settings that were used to create the report.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Relaunch Analysis;

Relaunch ByGroup

Syntax: obj << Relaunch ByGroup

Description: Opens the platform launch window and recalls the settings that were used to create the report.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Relaunch ByGroup;

Remove Column Switcher

Syntax: obj << Remove Column Switcher

Description: Removes the most recent Column Switcher that has been added to the platform.

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
    :marital status,
    {:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;

Remove Local Data Filter

Syntax: obj << Remove Local Data Filter

Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly

dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
    Nominal Distribution( Column( :country ) ),
    Local Data Filter(
        Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
        Mode( Show( 1 ), Include( 1 ) )
    )
);
Wait( 2 );
dist << remove local data filter;

Render Preset

Syntax: Render Preset( preset )

Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.

JMP Version Added: 18

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Render Preset( Expr( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) ) );

Report

Syntax: obj << Report;Report( obj )

Description: Returns a reference to the report object.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Report View

Syntax: obj << Report View( "Full"|"Summary" )

Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Report View( "Summary" );

Save ByGroup Script to Data Table

Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );

Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Save ByGroup Script to Data Table;

Save ByGroup Script to Journal

Syntax: obj << Save ByGroup Script to Journal

Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Save ByGroup Script to Journal;

Save ByGroup Script to Script Window

Syntax: obj << Save ByGroup Script to Script Window

Description: Create a JSL script to produce this analysis, and append it to the current Script text window.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Save ByGroup Script to Script Window;

Save Script for All Objects

Syntax: obj << Save Script for All Objects

Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Script for All Objects;

Save Script for All Objects To Data Table

Syntax: obj << Save Script for All Objects To Data Table( <name> )

Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.

Example 1

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    By( _bycol )
);
obj << Go;
obj[1] << Save Script for All Objects To Data Table( "My Script" );

Save Script to Data Table

Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );

Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );

Save Script to Journal

Syntax: obj << Save Script to Journal

Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Script to Journal;

Save Script to Report

Syntax: obj << Save Script to Report

Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Script to Report;

Save Script to Script Window

Syntax: obj << Save Script to Script Window

Description: Create a JSL script to produce this analysis, and append it to the current Script text window.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Script to Script Window;

SendToByGroup

Syntax: SendToByGroup( {":Column == level"}, command );

Description: Sends platform commands or display customization commands to each level of a by-group.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    By( :Sex ),
    SendToByGroup(
        {:sex == "F"},
        Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) )
    ),
    SendToByGroup( {:sex == "M"}, Continuous Distribution( Column( :weight ) ) )
);

SendToEmbeddedScriptable

Syntax: SendToEmbeddedScriptable( Dispatch( "Outline name", "Element name", command );

Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.

dt = Open( "$SAMPLE_DATA/Reliability/Fan.jmp" );
dt << Life Distribution(
    Y( :Time ),
    Censor( :Censor ),
    Censor Code( 1 ),
    <<Fit Weibull,
    SendToEmbeddedScriptable(
        Dispatch(
            {"Statistics", "Parametric Estimate - Weibull", "Profilers", "Density Profiler"},
            {1, Confidence Intervals( 0 ), Term Value( Time( 6000, Lock( 0 ), Show( 1 ) ) )}
        )
    )
);

SendToReport

Syntax: SendToReport( Dispatch( "Outline name", "Element name", Element type, command );

Description: Send To Report is used in tandem with the Dispatch command to customize the appearance of a report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    Nominal Distribution( Column( :age ) ),
    Continuous Distribution( Column( :weight ) ),
    SendToReport( Dispatch( "age", "Distrib Nom Hist", FrameBox, {Frame Size( 178, 318 )} ) )
);

Sync to Data Table Changes

Syntax: obj << Sync to Data Table Changes

Description: Sync with the exclude and data changes that have been made.

dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
Wait( 1 );
dt << Delete Rows( dt << Get Rows Where( :Region == "W" ) );
dist << Sync To Data Table Changes;

Title

Syntax: obj << Title( "new title" )

Description: Sets the title of the platform.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Title( "My Platform" );

Top Report

Syntax: obj << Top Report

Description: Returns a reference to the root node in the report.

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Transform Column

Syntax: obj = <Platform>(... Transform Column(<name>, Formula(<expression>), [Random Seed(<n>)], [Numeric|Character|Expression], [Continuous|Nominal|Ordinal|Unstructured Text], [column properties]) ...)

Description: Create a transform column in the local context of an object, usually a platform. The transform column is active only for the lifetime of the platform.

JMP Version Added: 16

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
    Transform Column( "age^2", Format( "Fixed Dec", 5, 0 ), Formula( :age * :age ) ),
    Continuous Distribution( Column( :"age^2"n ) )
);

View Web XML

Syntax: obj << View Web XML

Description: Returns the XML code that is used to create the interactive HTML report.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;

Window View

Syntax: obj = K Means Cluster(...Window View( "Visible"|"Invisible"|"Private" )...)

Description: Set the type of the window to be created for the report. By default a Visible report window will be created. An Invisible window will not appear on screen, but is discoverable by functions such as Window(). A Private window responds to most window messages but is not discoverable and must be addressed through the report object

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( Window View( "Private" ), Y( :weight ), X( :height ), Fit Line );
eqn = Report( biv )["Linear Fit", Text Edit Box( 1 )] << Get Text;
biv << Close Window;
New Window( "Bivariate Equation",
    Outline Box( "Big Class Linear Fit", Text Box( eqn, <<Set Base Font( "Title" ) ) )
);

K Means Fit

Item Messages

Biplot

Syntax: obj << Biplot( state=0|1 )

Description: Shows or hides a plot of the points and clusters in the first two principal components of the data.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot( 1 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot( 1 );

Biplot 3D

Syntax: obj << Biplot 3D( state=0|1 )

Description: Shows or hides a plot of the points and clusters in the first three principal components of the data.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot 3D( 1 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot 3D( 1 );

Biplot Contour Density

Syntax: obj << Biplot Contour Density( density percent )

Description: Sets the level for the density contour.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Biplot Contour Density( .95 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Biplot Contour Density( .95 );

Biplot Ray Position

Syntax: obj << Biplot Ray Position( [X, Y, scaling] )

Description: Moves the biplot ray display.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Biplot Ray Position( [-1, -1, 2] );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Biplot Ray Position( [-1, -1, 2] );

Get Statistics

Syntax: obj << Get Statistics

Description: Returns the mean and standard deviation for each variable within each cluster.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
stats = obj << Get Statistics;
Show( stats );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
stats = obj << Get Statistics;
Show( stats );

Mark Clusters

Syntax: obj << Mark Clusters

Description: Sets the markers in the row state for each row in the data table. Each cluster is assigned a different marker. This affects plots across platforms using rowstate markers, including the Biplot in Cluster.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot( 1 );
obj << Mark Clusters;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Biplot( 1 );
obj << Mark Clusters;

Parallel Coord Plots

Syntax: obj << Parallel Coord Plots( state=0|1 )

Description: Shows or hides a plot for each cluster separately showing connected line segments that represent each row of the data table.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Parallel Coord Plots( 1 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Parallel Coord Plots( 1 );

Publish Cluster Formulas

Syntax: obj << Publish Cluster Formulas

Description: Builds probability formulas and publishes them as a formula column script in Formula Depot.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Publish Cluster Formulas;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Publish Cluster Formulas;

SOM Heat Map

Syntax: obj << SOM Heat Map( state=0|1 )

Description: Shows or hides a heat map of the SOM cluster means, colored by one of the Y variables that was used in the clustering.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    {SOM N Rows( 2 ), SOM Bandwidth( 0.5 ), Single Step( 0 ), Number of Clusters( 4 ), SOM,
    Go}
);
obj << SOM Heat Map;

Save Cluster Distance

Syntax: obj << Save Cluster Distance

Description: Saves a column to the data table that contains the distance to the assigned cluster number.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Cluster Distance;

Save Cluster Formula

Syntax: obj << Save Cluster Formula

Description: Saves a column to the data table with a formula that determines the most likely cluster.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Cluster Formula;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Cluster Formula;

Save Clusters

Syntax: obj << Save Clusters

Description: Saves a new column to the data table that contains the most likely cluster for each row.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Clusters;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Clusters;

Save Colors to Table

Syntax: obj << Save Colors to Table

Description: Saves the color assigned to the row state in each row according to the cluster membership.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Colors to Table;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Save Colors to Table;

Save Distance Formula

Syntax: obj << Save Distance Formula

Description: Saves a column to the data table that contains the distance formula to the assigned cluster number.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save Distance Formula;

Save K Cluster Distances

Syntax: obj << Save K Cluster Distances

Description: Saves the distance to each cluster center as a separate column in the data table.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save K Cluster Distances;

Save K Distance Formulas

Syntax: obj << Save K Distance Formulas

Description: Saves the distance formula to each cluster center as a separate column in the data table.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Save K Distance Formulas;

Save SOM Grid

Syntax: obj << Save SOM Grid

Description: Saves new columns to the data table that contain the SOM grid row and column for the most likely cluster.

JMP Version Added: 17

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    {SOM N Rows( 2 ), SOM Bandwidth( 0.5 ), Single Step( 0 ), Number of Clusters( 4 ), SOM,
    Go}
);
obj << Save SOM Grid;

Scatterplot Matrix

Syntax: obj << Scatterplot Matrix( state=0|1 )

Description: Creates a scatterplot matrix in a new window with confidence ellipses based on the current number of clusters.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Scatterplot Matrix;

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Go;
obj << Scatterplot Matrix;

Show Biplot Rays

Syntax: obj << Show Biplot Rays( state=0|1 )

Description: Shows or hides the rays on the biplot. On by default.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Show Biplot Rays( 1 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go( Biplot( 1 ) )
);
obj << Show Biplot Rays( 1 );

Simulate Clusters

Syntax: obj << Simulate Clusters

Description: Creates a new data table with simulated data using the estimated cluster mixing probabilities, means, and standard deviations for each cluster.

JMP Version Added: 14

K Means Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Simulate Clusters( 1000 );

Normal Mixtures Example

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = Normal Mixtures(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 ),
    Go
);
obj << Simulate Clusters( 1000 );

Step

Syntax: obj << Step

Description: Takes one K-means step or iteration.

JMP Version Added: 14

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << K Means Cluster(
    Y( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Number of Clusters( 3 )
);
obj << Single Step( 1 );
obj << Go;
obj << Step;
Wait( 1 );
obj << Step;