simplekmeans.java

数据挖掘聚类算法：SimpleKMeans源代码

/*
 *    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.
 */

/*
 *    SimpleKMeans.java
 *    Copyright (C) 2000 University of Waikato, Hamilton, New Zealand
 *
 */
package weka.clusterers;

import weka.classifiers.rules.DecisionTableHashKey;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.ReplaceMissingValues;

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

/**
 <!-- globalinfo-start -->
 * Cluster data using the k means algorithm
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -N &lt;num&gt;
 *  number of clusters.
 *  (default 2).</pre>
 * 
 * <pre> -V
 *  Display std. deviations for centroids.
 * </pre>
 * 
 * <pre> -M
 *  Replace missing values with mean/mode.
 * </pre>
 * 
 * <pre> -S &lt;num&gt;
 *  Random number seed.
 *  (default 10)</pre>
 * 
 <!-- options-end -->
 *
 * @author Mark Hall ([e-mail])
 * @author Eibe Frank ([e-mail])
 * @version $Revision: 1.39 $
 * @see RandomizableClusterer
 */
public class SimpleKMeans 
  extends RandomizableClusterer 
  implements NumberOfClustersRequestable, WeightedInstancesHandler {

  /** for serialization */
  static final long serialVersionUID = -3235809600124455376L;
  
  /**
   * replace missing values in training instances
   */
  private ReplaceMissingValues m_ReplaceMissingFilter;

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

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

  /**
   * Holds the standard deviations of the numeric attributes in each cluster
   */
  private Instances m_ClusterStdDevs;

  
  /**
   * For each cluster, holds the frequency counts for the values of each 
   * nominal attribute
   */
  private int [][][] m_ClusterNominalCounts;
  private int[][] m_ClusterMissingCounts;
  
  /**
   * Stats on the full data set for comparison purposes
   */
  private double[] m_FullMeansOrModes;
  private double[] m_FullStdDevs;
  private int[][] m_FullNominalCounts;
  private int[] m_FullMissingCounts;

  /**
   * Display standard deviations for numeric atts
   */
  private boolean m_displayStdDevs;

  /**
   * Replace missing values globally?
   */
  private boolean m_dontReplaceMissing = false;

  /**
   * The number of instances in each cluster
   */
  private int [] m_ClusterSizes;

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

  /**
   * Keep track of the number of iterations completed before convergence
   */
  private int m_Iterations = 0;

  /**
   * Holds the squared errors for all clusters
   */
  private double [] m_squaredErrors;

  /**
   * the default constructor
   */
  public SimpleKMeans() {
    super();
    
    m_SeedDefault = 10;
    setSeed(m_SeedDefault);
  }
  
  /**
   * 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 k means algorithm";
  }

  /**
   * 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.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);

    m_Iterations = 0;

    m_ReplaceMissingFilter = new ReplaceMissingValues();
    Instances instances = new Instances(data);
    instances.setClassIndex(-1);
    if (!m_dontReplaceMissing) {
      m_ReplaceMissingFilter.setInputFormat(instances);
      instances = Filter.useFilter(instances, m_ReplaceMissingFilter);
    }

    m_FullMeansOrModes = new double[instances.numAttributes()];
    m_FullMissingCounts = new int[instances.numAttributes()];
    if (m_displayStdDevs) {
      m_FullStdDevs = new double[instances.numAttributes()];
    }
    m_FullNominalCounts = new int[instances.numAttributes()][0];
    for (int i = 0; i < instances.numAttributes(); i++) {
      m_FullMissingCounts[i] = instances.attributeStats(i).missingCount;
      m_FullMeansOrModes[i] = instances.meanOrMode(i);
      if (instances.attribute(i).isNumeric()) {
        if (m_displayStdDevs) {
          m_FullStdDevs[i] = Math.sqrt(instances.variance(i));
        }
        if (m_FullMissingCounts[i] == instances.numInstances()) {
          m_FullMeansOrModes[i] = Double.NaN; // mark missing as mean
        }
      } else {
        m_FullNominalCounts[i] = instances.attributeStats(i).nominalCounts;
        if (m_FullMissingCounts[i] 
            > m_FullNominalCounts[i][Utils.maxIndex(m_FullNominalCounts[i])]) {
          m_FullMeansOrModes[i] = -1; // mark missing as most common value
        }
      }
    }

    m_Min = new double [instances.numAttributes()];
    m_Max = new double [instances.numAttributes()];
    for (int i = 0; i < instances.numAttributes(); i++) {
      m_Min[i] = m_Max[i] = Double.NaN;
    }
    
    m_ClusterCentroids = new Instances(instances, m_NumClusters);
    int[] clusterAssignments = new int [instances.numInstances()];

    for (int i = 0; i < instances.numInstances(); i++) {
      updateMinMax(instances.instance(i));
    }
    
    Random RandomO = new Random(getSeed());
    int instIndex;
    HashMap initC = new HashMap();
    DecisionTableHashKey hk = null;

    for (int j = instances.numInstances() - 1; j >= 0; j--) {
      instIndex = RandomO.nextInt(j+1);
      hk = new DecisionTableHashKey(instances.instance(instIndex), 
				     instances.numAttributes(), true);
      if (!initC.containsKey(hk)) {
	m_ClusterCentroids.add(instances.instance(instIndex));
	initC.put(hk, null);
      }
      instances.swap(j, instIndex);
      
      if (m_ClusterCentroids.numInstances() == m_NumClusters) {
	break;
      }
    }

    m_NumClusters = m_ClusterCentroids.numInstances();
    
    int i;
    boolean converged = false;
    int emptyClusterCount;
    Instances [] tempI = new Instances[m_NumClusters];
    m_squaredErrors = new double [m_NumClusters];
    m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
    m_ClusterMissingCounts = new int[m_NumClusters][instances.numAttributes()];
    while (!converged) {
      emptyClusterCount = 0;
      m_Iterations++;
      converged = true;
      for (i = 0; i < instances.numInstances(); i++) {
	Instance toCluster = instances.instance(i);
	int newC = clusterProcessedInstance(toCluster, true);
	if (newC != clusterAssignments[i]) {
	  converged = false;
	}
	clusterAssignments[i] = newC;
      }
      
      // update centroids
      m_ClusterCentroids = new Instances(instances, m_NumClusters);
      for (i = 0; i < m_NumClusters; i++) {
	tempI[i] = new Instances(instances, 0);
      }
      for (i = 0; i < instances.numInstances(); i++) {
	tempI[clusterAssignments[i]].add(instances.instance(i));
      }
      for (i = 0; i < m_NumClusters; i++) {
	double [] vals = new double[instances.numAttributes()];
	if (tempI[i].numInstances() == 0) {
	  // empty cluster
	  emptyClusterCount++;
	} else {
	  for (int j = 0; j < instances.numAttributes(); j++) {
	    vals[j] = tempI[i].meanOrMode(j);
            m_ClusterMissingCounts[i][j] = tempI[i].attributeStats(j).missingCount;
	    m_ClusterNominalCounts[i][j] = 
	      tempI[i].attributeStats(j).nominalCounts;
            if (tempI[i].attribute(j).isNominal()) {
              if (m_ClusterMissingCounts[i][j] >  
                  m_ClusterNominalCounts[i][j][Utils.maxIndex(m_ClusterNominalCounts[i][j])]) {
                vals[j] = Instance.missingValue(); // mark mode as missing
              }
            } else {
              if (m_ClusterMissingCounts[i][j] == tempI[i].numInstances()) {
                vals[j] = Instance.missingValue(); // mark mean as missing
              }
            }
	  }
	  m_ClusterCentroids.add(new Instance(1.0, vals));
	}
      }

      if (emptyClusterCount > 0) {
	m_NumClusters -= emptyClusterCount;
        if (converged) {
          Instances[] t = new Instances[m_NumClusters];
          int index = 0;
          for (int k = 0; k < tempI.length; k++) {
            if (tempI[k].numInstances() > 0) {
              t[index++] = tempI[k];
            }
          }
          tempI = t;
        } else {
          tempI = new Instances[m_NumClusters];
        }
      }
      if (!converged) {
	m_squaredErrors = new double [m_NumClusters];
	m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
      }
    }
    if (m_displayStdDevs) {
      m_ClusterStdDevs = new Instances(instances, m_NumClusters);
    }
    m_ClusterSizes = new int [m_NumClusters];
    for (i = 0; i < m_NumClusters; i++) {
      if (m_displayStdDevs) {
        double [] vals2 = new double[instances.numAttributes()];
        for (int j = 0; j < instances.numAttributes(); j++) {
          if (instances.attribute(j).isNumeric()) {
            vals2[j] = Math.sqrt(tempI[i].variance(j));
          } else {
            vals2[j] = Instance.missingValue();
          }	
        }    
        m_ClusterStdDevs.add(new Instance(1.0, vals2));
      }
      m_ClusterSizes[i] = tempI[i].numInstances();
    }
  }

  /**
   * clusters an instance that has been through the filters
   *
   * @param instance the instance to assign a cluster to
   * @param updateErrors if true, update the within clusters sum of errors
   * @return a cluster number
   */
  private int clusterProcessedInstance(Instance instance, boolean updateErrors) {
    double minDist = Integer.MAX_VALUE;
    int bestCluster = 0;
    for (int i = 0; i < m_NumClusters; i++) {
      double dist = distance(instance, m_ClusterCentroids.instance(i));
      if (dist < minDist) {
	minDist = dist;
	bestCluster = i;
      }