weka.clusterers

## Class HierarchicalClusterer

- java.lang.Object
- - weka.clusterers.AbstractClusterer
  - - weka.clusterers.HierarchicalClusterer

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Clusterer, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler

      public class HierarchicalClusterer
      extends AbstractClusterer
      implements OptionHandler, Drawable

  Hierarchical clustering class. Implements a number of classic hierarchical clustering methods. Valid options are:

       -N
        number of clusters

       -L
        Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining)
        [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMPLETE|NEIGHBOR_JOINING]

       -A
       Distance function to use. (default: weka.core.EuclideanDistance)

       -P
       Print hierarchy in Newick format, which can be used for display in other programs.

       -D
       If set, classifier is run in debug mode and may output additional info to the console.

       -B
       \If set, distance is interpreted as branch length, otherwise it is node height.

  Version:
    $Revision: 12475 $
  Author:
    Remco Bouckaert (rrb@xm.co.nz, remco@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static Tag[]` | `TAGS_LINK_TYPE`

    - ### Fields inherited from interface weka.core.Drawable

      `BayesNet, Newick, NOT_DRAWABLE, TREE`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `HierarchicalClusterer()`

  - ### Method Summary

    All Methods Static Methods Instance Methods Concrete Methods
    Modifier and Type | Method and Description
    --- | ---
    `void` | `buildClusterer(Instances data)` Generates a clusterer.
    `int` | `clusterInstance(Instance instance)` Classifies a given instance.
    `java.lang.String` | `distanceFunctionTipText()`
    `java.lang.String` | `distanceIsBranchLengthTipText()`
    `double[]` | `distributionForInstance(Instance instance)` Predicts the cluster memberships for a given instance.
    `Capabilities` | `getCapabilities()` Returns the Capabilities of this clusterer.
    `DistanceFunction` | `getDistanceFunction()`
    `boolean` | `getDistanceIsBranchLength()`
    `SelectedTag` | `getLinkType()`
    `int` | `getNumClusters()`
    `java.lang.String[]` | `getOptions()` Gets the current settings of the clusterer.
    `boolean` | `getPrintNewick()`
    `java.lang.String` | `getRevision()` Returns the revision string.
    `java.lang.String` | `globalInfo()` This will return a string describing the clusterer.
    `java.lang.String` | `graph()` Returns a string that describes a graph representing the object.
    `int` | `graphType()` Returns the type of graph representing the object.
    `java.lang.String` | `linkTypeTipText()`
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `static void` | `main(java.lang.String[] argv)`
    `int` | `numberOfClusters()` Returns the number of clusters.
    `java.lang.String` | `numClustersTipText()`
    `java.lang.String` | `printNewickTipText()`
    `void` | `setDistanceFunction(DistanceFunction distanceFunction)`
    `void` | `setDistanceIsBranchLength(boolean bDistanceIsHeight)`
    `void` | `setLinkType(SelectedTag newLinkType)`
    `void` | `setNumClusters(int nClusters)`
    `void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
    `void` | `setPrintNewick(boolean bPrintNewick)`
    `java.lang.String` | `toString()`

    - ### Methods inherited from class weka.clusterers.AbstractClusterer

      `debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, postExecution, preExecution, run, runClusterer, setDebug, setDoNotCheckCapabilities`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Field Detail

    - #### TAGS_LINK_TYPE

          public static final Tag[] TAGS_LINK_TYPE

  - ### Constructor Detail

    - #### HierarchicalClusterer

          public HierarchicalClusterer()

  - ### Method Detail

    - #### setNumClusters

          public void setNumClusters(int nClusters)

    - #### getNumClusters

          public int getNumClusters()

    - #### getDistanceFunction

          public DistanceFunction getDistanceFunction()

    - #### setDistanceFunction

          public void setDistanceFunction(DistanceFunction distanceFunction)

    - #### getPrintNewick

          public boolean getPrintNewick()

    - #### setPrintNewick

          public void setPrintNewick(boolean bPrintNewick)

    - #### setLinkType

          public void setLinkType(SelectedTag newLinkType)

    - #### getLinkType

          public SelectedTag getLinkType()

    - #### buildClusterer

          public void buildClusterer(Instances data)
                              throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
      Specified by:
        `buildClusterer` in interface `Clusterer`
      Specified by:
        `buildClusterer` in class `AbstractClusterer`
      Parameters:
        `data` - set of instances serving as training data
      Throws:
        `java.lang.Exception` - if the clusterer has not been generated successfully

    - #### clusterInstance

          public int clusterInstance(Instance instance)
                              throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      Classifies a given instance. Either this or distributionForInstance() needs to be implemented by subclasses.
      Specified by:
        `clusterInstance` in interface `Clusterer`
      Overrides:
        `clusterInstance` in class `AbstractClusterer`
      Parameters:
        `instance` - the instance to be assigned to a cluster
      Returns:
        the number of the assigned cluster as an integer
      Throws:
        `java.lang.Exception` - if instance could not be clustered successfully

    - #### distributionForInstance

          public double[] distributionForInstance(Instance instance)
                                           throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      Predicts the cluster memberships for a given instance. Either this or clusterInstance() needs to be implemented by subclasses.
      Specified by:
        `distributionForInstance` in interface `Clusterer`
      Overrides:
        `distributionForInstance` in class `AbstractClusterer`
      Parameters:
        `instance` - the instance to be assigned a cluster.
      Returns:
        an array containing the estimated membership probabilities of the test instance in each cluster (this should sum to at most 1)
      Throws:
        `java.lang.Exception` - if distribution could not be computed successfully

    - #### getCapabilities

          public Capabilities getCapabilities()

      Description copied from class: `AbstractClusterer`
      Returns the Capabilities of this clusterer. Derived clusterers have to override this method to enable capabilities.
      Specified by:
        `getCapabilities` in interface `Clusterer`
      Specified by:
        `getCapabilities` in interface `CapabilitiesHandler`
      Overrides:
        `getCapabilities` in class `AbstractClusterer`
      Returns:
        the capabilities of this object
      See Also:
        `Capabilities`

    - #### numberOfClusters

          public int numberOfClusters()
                               throws java.lang.Exception

      Description copied from class: `AbstractClusterer`
      Returns the number of clusters.
      Specified by:
        `numberOfClusters` in interface `Clusterer`
      Specified by:
        `numberOfClusters` in class `AbstractClusterer`
      Returns:
        the number of clusters generated for a training dataset.
      Throws:
        `java.lang.Exception` - if number of clusters could not be returned successfully

    - #### listOptions

          public java.util.Enumeration<Option> listOptions()

      Returns an enumeration describing the available options.
      Specified by:
        `listOptions` in interface `OptionHandler`
      Overrides:
        `listOptions` in class `AbstractClusterer`
      Returns:
        an enumeration of all the available options.

    - #### setOptions

          public void setOptions(java.lang.String[] options)
                          throws java.lang.Exception

      Parses a given list of options.

      Valid options are:

      Specified by:
        `setOptions` in interface `OptionHandler`
      Overrides:
        `setOptions` in class `AbstractClusterer`
      Parameters:
        `options` - the list of options as an array of strings
      Throws:
        `java.lang.Exception` - if an option is not supported

    - #### getOptions

          public java.lang.String[] getOptions()

      Gets the current settings of the clusterer.
      Specified by:
        `getOptions` in interface `OptionHandler`
      Overrides:
        `getOptions` in class `AbstractClusterer`
      Returns:
        an array of strings suitable for passing to setOptions()

    - #### toString

          public java.lang.String toString()

      Overrides:
        `toString` in class `java.lang.Object`

    - #### getDistanceIsBranchLength

          public boolean getDistanceIsBranchLength()

    - #### setDistanceIsBranchLength

          public void setDistanceIsBranchLength(boolean bDistanceIsHeight)

    - #### distanceIsBranchLengthTipText

          public java.lang.String distanceIsBranchLengthTipText()

    - #### numClustersTipText

          public java.lang.String numClustersTipText()

      Returns:
        a string to describe the NumClusters

    - #### printNewickTipText

          public java.lang.String printNewickTipText()

      Returns:
        a string to describe the print Newick flag

    - #### distanceFunctionTipText

          public java.lang.String distanceFunctionTipText()

      Returns:
        a string to describe the distance function

    - #### linkTypeTipText

          public java.lang.String linkTypeTipText()

      Returns:
        a string to describe the Link type

    - #### globalInfo

          public java.lang.String globalInfo()

      This will return a string describing the clusterer.
      Returns:
        The string.

    - #### main

          public static void main(java.lang.String[] argv)

    - #### graph

          public java.lang.String graph()
                                 throws java.lang.Exception

      Description copied from interface: `Drawable`
      Returns a string that describes a graph representing the object. The string should be in XMLBIF ver. 0.3 format if the graph is a BayesNet, otherwise it should be in dotty format.
      Specified by:
        `graph` in interface `Drawable`
      Returns:
        the graph described by a string
      Throws:
        `java.lang.Exception` - if the graph can't be computed

    - #### graphType

          public int graphType()

      Description copied from interface: `Drawable`
      Returns the type of graph representing the object.
      Specified by:
        `graphType` in interface `Drawable`
      Returns:
        the type of graph representing the object

    - #### getRevision

          public java.lang.String getRevision()

      Returns the revision string.
      Specified by:
        `getRevision` in interface `RevisionHandler`
      Overrides:
        `getRevision` in class `AbstractClusterer`
      Returns:
        the revision