Package weka.clusterers

Interface Clusterer

All Known Subinterfaces:
DensityBasedClusterer
All Known Implementing Classes:
AbstractClusterer, AbstractDensityBasedClusterer, Canopy, Cobweb, EM, FarthestFirst, FilteredClusterer, HierarchicalClusterer, MakeDensityBasedClusterer, RandomizableClusterer, RandomizableDensityBasedClusterer, RandomizableSingleClustererEnhancer, SimpleKMeans, SingleClustererEnhancer
public interface Clusterer
Interface for clusterers. Clients will typically extend either AbstractClusterer or AbstractDensityBasedClusterer.
Version:
$Revision: 8034 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)

  • Method Details

    • buildClusterer

      void buildClusterer(Instances data) throws Exception
      Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via options.
      Parameters:
      data - set of instances serving as training data
      Throws:
      Exception - if the clusterer has not been generated successfully

    • clusterInstance

      int clusterInstance(Instance instance) throws Exception
      Classifies a given instance. Either this or distributionForInstance() needs to be implemented by subclasses.
      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

      double[] distributionForInstance(Instance instance) throws Exception
      Predicts the cluster memberships for a given instance. Either this or clusterInstance() needs to be implemented by subclasses.
      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

    • numberOfClusters

      int numberOfClusters() throws Exception
      Returns the number of clusters.
      Returns:
      the number of clusters generated for a training dataset.
      Throws:
      Exception - if number of clusters could not be returned successfully

    • getCapabilities

      Capabilities getCapabilities()
      Returns the Capabilities of this clusterer. Derived classifiers have to override this method to enable capabilities.
      Returns:
      the capabilities of this object
      See Also: