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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

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/*
 *   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 3 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, see .
 */

/*
 *    DensityBasedClusterer.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.clusterers;

import weka.core.Instance;

/**
 * Interface for clusterers that can estimate the density for a given instance.
 * Implementations will typically extend AbstractDensityBasedClusterer.
 * 
 * @author Mark Hall ([email protected])
 * @author Eibe Frank ([email protected])
 * @version $Revision: 9379 $
 */
public interface DensityBasedClusterer extends Clusterer {

  /**
   * Returns the prior probability of each cluster.
   * 
   * @return the prior probability for each cluster
   * @exception Exception if priors could not be returned successfully
   */
  double[] clusterPriors() throws Exception;

  /**
   * Computes the log of the conditional density (per cluster) for a given
   * instance.
   * 
   * @param instance the instance to compute the density for
   * @return an array containing the estimated densities
   * @exception Exception if the density could not be computed successfully
   */
  double[] logDensityPerClusterForInstance(Instance instance) throws Exception;

  /**
   * Computes the density for a given instance.
   * 
   * @param instance the instance to compute the density for
   * @return the density.
   * @exception Exception if the density could not be computed successfully
   */
  double logDensityForInstance(Instance instance) throws Exception;

  /**
   * Returns the logs of the joint densities for a given instance.
   * 
   * @param inst the instance
   * @return the array of values
   * @exception Exception if values could not be computed
   */
  double[] logJointDensitiesForInstance(Instance inst) throws Exception;

  /**
   * Returns the cluster probability distribution for an instance.
   * 
   * @param instance the instance to be clustered
   * @return the probability distribution
   * @throws Exception if computation fails
   */
  @Override
  double[] distributionForInstance(Instance instance) throws Exception;
}