content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    Clusterer.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.clusterers;
  
  import weka.core.Capabilities;
  import weka.core.Instance;
  import weka.core.Instances;
  
  /**
   * Interface for clusterers. Clients will typically extend either
   * AbstractClusterer or AbstractDensityBasedClusterer.
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @version $Revision: 5987 $
   */
  public interface Clusterer {
  
    /**
     * Generates a clusterer. Has to initialize all fields of the clusterer
     * that are not being set via options.
     *
     * @param data set of instances serving as training data 
     * @exception Exception if the clusterer has not been 
     * generated successfully
     */
    void buildClusterer(Instances data) throws Exception;
  
    /**
     * Classifies a given instance. Either this or distributionForInstance()
     * needs to be implemented by subclasses.
     *
     * @param instance the instance to be assigned to a cluster
     * @return the number of the assigned cluster as an integer
     * @exception Exception if instance could not be clustered
     * successfully
     */
    int clusterInstance(Instance instance) throws Exception;
  
    /**
     * Predicts the cluster memberships for a given instance.  Either
     * this or clusterInstance() needs to be implemented by subclasses.
     *
     * @param instance the instance to be assigned a cluster.
     * @return an array containing the estimated membership 
     * probabilities of the test instance in each cluster (this 
     * should sum to at most 1)
     * @exception Exception if distribution could not be 
     * computed successfully 
     */
    public double[] distributionForInstance(Instance instance) throws Exception;
  
    /**
     * Returns the number of clusters.
     *
     * @return the number of clusters generated for a training dataset.
     * @exception Exception if number of clusters could not be returned
     * successfully
     */
    int numberOfClusters() throws Exception;
  
    /** 
     * Returns the Capabilities of this clusterer. Derived classifiers have to
     * override this method to enable capabilities.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities();
    
  }
  