farthestfirst.java

数据挖掘中聚类的算法

/*
 *    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.
 */

/*
 *    FarthestFirst.java
 *    Copyright (C) 2002 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.clusterers;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 <!-- globalinfo-start -->
 * Cluster data using the FarthestFirst algorithm.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. Mathematics of Operations Research. 10(2):180-184.<br/>
 * <br/>
 * Sanjoy Dasgupta: Performance Guarantees for Hierarchical Clustering. In: 15th Annual Conference on Computational Learning Theory, 351-363, 2002.<br/>
 * <br/>
 * Notes:<br/>
 * - works as a fast simple approximate clusterer<br/>
 * - modelled after SimpleKMeans, might be a useful initializer for it
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- technical-bibtex-start -->
 * BibTeX:
 * <pre>
 * &#64;article{Hochbaum1985,
 *    author = {Hochbaum and Shmoys},
 *    journal = {Mathematics of Operations Research},
 *    number = {2},
 *    pages = {180-184},
 *    title = {A best possible heuristic for the k-center problem},
 *    volume = {10},
 *    year = {1985}
 * }
 * 
 * &#64;inproceedings{Dasgupta2002,
 *    author = {Sanjoy Dasgupta},
 *    booktitle = {15th Annual Conference on Computational Learning Theory},
 *    pages = {351-363},
 *    publisher = {Springer},
 *    title = {Performance Guarantees for Hierarchical Clustering},
 *    year = {2002}
 * }
 * </pre>
 * <p/>
 <!-- technical-bibtex-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -N &lt;num&gt;
 *  number of clusters. (default = 2).</pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 1)</pre>
 * 
 <!-- options-end -->
 *
 * @author Bernhard Pfahringer ([e-mail])
 * @version $Revision: 1.8 $
 * @see RandomizableClusterer
 */
public class FarthestFirst 
  extends RandomizableClusterer 
  implements TechnicalInformationHandler {

  //Todo: rewrite to be fully incremental
  //      cleanup, like deleting m_instances 

  /** for serialization */
  static final long serialVersionUID = 7499838100631329509L;
  
  /**
   * training instances, not necessary to keep, 
   * could be replaced by m_ClusterCentroids where needed for header info
   */
  protected Instances m_instances;

  /**
   * replace missing values in training instances
   */
  protected ReplaceMissingValues m_ReplaceMissingFilter;

  /**
   * number of clusters to generate
   */
  protected int m_NumClusters = 2;

  /**
   * holds the cluster centroids
   */
  protected Instances m_ClusterCentroids;

  /**
   * attribute min values
   */
  private double [] m_Min;
  
  /**
   * attribute max values
   */
  private double [] m_Max;

  /**
   * Returns a string describing this clusterer
   * @return a description of the evaluator suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Cluster data using the FarthestFirst algorithm.\n\n"
      + "For more information see:\n\n"
      + getTechnicalInformation().toString() + "\n\n"
      + "Notes:\n"
      + "- works as a fast simple approximate clusterer\n"
      + "- modelled after SimpleKMeans, might be a useful initializer for it";
  }

  /**
   * Returns an instance of a TechnicalInformation object, containing 
   * detailed information about the technical background of this class,
   * e.g., paper reference or book this class is based on.
   * 
   * @return the technical information about this class
   */
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation 	result;
    TechnicalInformation 	additional;
    
    result = new TechnicalInformation(Type.ARTICLE);
    result.setValue(Field.AUTHOR, "Hochbaum and Shmoys");
    result.setValue(Field.YEAR, "1985");
    result.setValue(Field.TITLE, "A best possible heuristic for the k-center problem");
    result.setValue(Field.JOURNAL, "Mathematics of Operations Research");
    result.setValue(Field.VOLUME, "10");
    result.setValue(Field.NUMBER, "2");
    result.setValue(Field.PAGES, "180-184");
    
    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Sanjoy Dasgupta");
    additional.setValue(Field.TITLE, "Performance Guarantees for Hierarchical Clustering");
    additional.setValue(Field.BOOKTITLE, "15th Annual Conference on Computational Learning Theory");
    additional.setValue(Field.YEAR, "2002");
    additional.setValue(Field.PAGES, "351-363");
    additional.setValue(Field.PUBLISHER, "Springer");
    
    return result;
  }

  /**
   * Returns default capabilities of the clusterer.
   *
   * @return      the capabilities of this clusterer
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    return result;
  }

  /**
   * 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 
   * @throws Exception if the clusterer has not been 
   * generated successfully
   */
  public void buildClusterer(Instances data) throws Exception {

    // can clusterer handle the data?
    getCapabilities().testWithFail(data);

    //long start = System.currentTimeMillis();

    m_ReplaceMissingFilter = new ReplaceMissingValues();
    m_ReplaceMissingFilter.setInputFormat(data);
    m_instances = Filter.useFilter(data, m_ReplaceMissingFilter);

    initMinMax(m_instances);

    m_ClusterCentroids = new Instances(m_instances, m_NumClusters);

    int n = m_instances.numInstances();
    Random r = new Random(getSeed());
    boolean[] selected = new boolean[n];
    double[] minDistance = new double[n];

    for(int i = 0; i<n; i++) minDistance[i] = Double.MAX_VALUE;

    int firstI = r.nextInt(n);
    m_ClusterCentroids.add(m_instances.instance(firstI));
    selected[firstI] = true;

    updateMinDistance(minDistance,selected,m_instances,m_instances.instance(firstI));

    if (m_NumClusters > n) m_NumClusters = n;

    for(int i = 1; i < m_NumClusters; i++) {
      int nextI =  farthestAway(minDistance, selected);
      m_ClusterCentroids.add(m_instances.instance(nextI));
      selected[nextI] = true;
      updateMinDistance(minDistance,selected,m_instances,m_instances.instance(nextI));
    }

    m_instances = new Instances(m_instances,0);
    //long end = System.currentTimeMillis();
    //System.out.println("Clustering Time = " + (end-start));
  }


  protected void updateMinDistance(double[] minDistance, boolean[] selected, 
				   Instances data, Instance center) {
    for(int i = 0; i<selected.length; i++) 
      if (!selected[i]) {
	double d = distance(center,data.instance(i));
	if (d<minDistance[i]) 
	  minDistance[i] = d;
      }
  }

  protected int farthestAway(double[] minDistance, boolean[] selected) {
    double maxDistance = -1.0;
    int maxI = -1;
    for(int i = 0; i<selected.length; i++) 
      if (!selected[i]) 
	if (maxDistance < minDistance[i]) {
	  maxDistance = minDistance[i];
	  maxI = i;
	}
    return maxI;
  }

  protected void initMinMax(Instances data) {
    m_Min = new double [data.numAttributes()];
    m_Max = new double [data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
      m_Min[i] = m_Max[i] = Double.NaN;
    }

    for (int i = 0; i < data.numInstances(); i++) {
      updateMinMax(data.instance(i));
    }
  }


  /**
   * Updates the minimum and maximum values for all the attributes
   * based on a new instance.
   *
   * @param instance the new instance
   */
  private void updateMinMax(Instance instance) {  

    for (int j = 0;j < instance.numAttributes(); j++) {
      if (Double.isNaN(m_Min[j])) {
	m_Min[j] = instance.value(j);
	m_Max[j] = instance.value(j);
      } else {
	if (instance.value(j) < m_Min[j]) {
	  m_Min[j] = instance.value(j);
	} else {
	  if (instance.value(j) > m_Max[j]) {
	    m_Max[j] = instance.value(j);