/*
* 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)
* @revision $Revision: 1.18 $
*/
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();
}