Package weka.clusterers

Class HierarchicalClusterer

java.lang.Object
weka.clusterers.AbstractClusterer
weka.clusterers.HierarchicalClusterer
All Implemented Interfaces:
Serializable, 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: 15519 $
Author:
Remco Bouckaert (rrb@xm.co.nz, remco@cs.waikato.ac.nz), Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:

  • Field Details

  • Constructor Details

    • HierarchicalClusterer

      public HierarchicalClusterer()

  • Method Details

    • 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 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:
      Exception - if the clusterer has not been generated successfully

    • clusterInstance

      public int clusterInstance(Instance instance) throws 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:
      Exception - if instance could not be clustered successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws 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:
      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 CapabilitiesHandler
      Specified by:
      getCapabilities in interface Clusterer
      Overrides:
      getCapabilities in class AbstractClusterer
      Returns:
      the capabilities of this object
      See Also:

    • numberOfClusters

      public int numberOfClusters() throws 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:
      Exception - if number of clusters could not be returned successfully

    • listOptions

      public 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(String[] options) throws 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:
      Exception - if an option is not supported

    • getOptions

      public 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 String toString()
      Overrides:
      toString in class Object

    • getDistanceIsBranchLength

      public boolean getDistanceIsBranchLength()

    • setDistanceIsBranchLength

      public void setDistanceIsBranchLength(boolean bDistanceIsHeight)

    • distanceIsBranchLengthTipText

      public String distanceIsBranchLengthTipText()

    • numClustersTipText

      public String numClustersTipText()
      Returns:
      a string to describe the NumClusters

    • printNewickTipText

      public String printNewickTipText()
      Returns:
      a string to describe the print Newick flag

    • distanceFunctionTipText

      public String distanceFunctionTipText()
      Returns:
      a string to describe the distance function

    • linkTypeTipText

      public String linkTypeTipText()
      Returns:
      a string to describe the Link type

    • globalInfo

      public String globalInfo()
      This will return a string describing the clusterer.
      Returns:
      The string.

    • main

      public static void main(String[] argv)

    • graph

      public String graph() throws 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:
      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 String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class AbstractClusterer
      Returns:
      the revision