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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* 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.
*/
/*
* Copyright (C) 2008
* & Alexander Smirnov ([email protected])
*/
package weka.clusterers;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Enumeration;
import java.util.HashMap;
import java.util.Vector;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
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;
/**
* Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 682-687, 2002.
*
*
* BibTeX:
*
* @inproceedings{Yang2002,
* author = {Yiling Yang and Xudong Guan and Jinyuan You},
* booktitle = {Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining},
* pages = {682-687},
* publisher = {ACM New York, NY, USA},
* title = {CLOPE: a fast and effective clustering algorithm for transactional data},
* year = {2002}
* }
*
*
*
* Valid options are:
*
* -R <num>
* Repulsion
* (default 2.6)
*
*
* @author Alexander Smirnov ([email protected])
* @version $Revision: 5538 $
*/
public class CLOPE
extends AbstractClusterer
implements OptionHandler, TechnicalInformationHandler {
/** for serialization */
static final long serialVersionUID = -567567567567588L;
/**
* Inner class for cluster of CLOPE.
*
* @see Serializable
*/
private class CLOPECluster implements Serializable {
/**
* Number of transactions
*/
public int N = 0; //number of transactions
/**
* Number of distinct items (or width)
*/
public int W = 0;
/**
* Size of cluster
*/
public int S = 0;
/**
* Hash of pairs
*/
public HashMap occ = new HashMap();
/**
* Add item to cluster
*/
public void AddItem(String Item) {
int count;
if (!this.occ.containsKey(Item)) {
this.occ.put(Item, 1);
} else {
count = (Integer) this.occ.get(Item);
count++;
this.occ.remove(Item);
this.occ.put(Item, count);
}
this.S++;
}
public void AddItem(Integer Item) {
int count;
if (!this.occ.containsKey(Item)) {
this.occ.put(Item, 1);
} else {
count = (Integer) this.occ.get(Item);
count++;
this.occ.remove(Item);
this.occ.put(Item, count);
}
this.S++;
}
/**
* Delete item from cluster
*/
public void DeleteItem(String Item) {
int count;
count = (Integer) this.occ.get(Item);
if (count == 1) {
this.occ.remove(Item);
} else {
count--;
this.occ.remove(Item);
this.occ.put(Item, count);
}
this.S--;
}
public void DeleteItem(Integer Item) {
int count;
count = (Integer) this.occ.get(Item);
if (count == 1) {
this.occ.remove(Item);
} else {
count--;
this.occ.remove(Item);
this.occ.put(Item, count);
}
this.S--;
}
/**
* Calculate Delta
*/
public double DeltaAdd(Instance inst, double r) {
//System.out.println("DeltaAdd");
int S_new;
int W_new;
double profit;
double profit_new;
double deltaprofit;
S_new = 0;
W_new = occ.size();
if (inst instanceof SparseInstance) {
//System.out.println("DeltaAddSparceInstance");
for (int i = 0; i < inst.numValues(); i++) {
S_new++;
if ((Integer) this.occ.get(inst.index(i)) == null) {
W_new++;
}
}
} else {
for (int i = 0; i < inst.numAttributes(); i++) {
if (!inst.isMissing(i)) {
S_new++;
if ((Integer) this.occ.get(i + inst.toString(i)) == null) {
W_new++;
}
}
}
}
S_new += S;
if (N == 0) {
deltaprofit = S_new / Math.pow(W_new, r);
} else {
profit = S * N / Math.pow(W, r);
profit_new = S_new * (N + 1) / Math.pow(W_new, r);
deltaprofit = profit_new - profit;
}
return deltaprofit;
}
/**
* Add instance to cluster
*/
public void AddInstance(Instance inst) {
if (inst instanceof SparseInstance) {
// System.out.println("AddSparceInstance");
for (int i = 0; i < inst.numValues(); i++) {
AddItem(inst.index(i));
// for(int i=0;i clusters = new ArrayList();
/**
* Specifies the repulsion default
*/
protected double m_RepulsionDefault = 2.6;
/**
* Specifies the repulsion
*/
protected double m_Repulsion = m_RepulsionDefault;
/**
* Number of clusters
*/
protected int m_numberOfClusters = -1;
/**
* Counter for the processed instances
*/
protected int m_processed_InstanceID;
/**
* Number of instances
*/
protected int m_numberOfInstances;
/**
*
*/
protected ArrayList m_clusterAssignments = new ArrayList();
/**
* whether the number of clusters was already determined
*/
protected boolean m_numberOfClustersDetermined = false;
public int numberOfClusters() {
determineNumberOfClusters();
return m_numberOfClusters;
}
protected void determineNumberOfClusters() {
m_numberOfClusters = clusters.size();
m_numberOfClustersDetermined = true;
}
public Enumeration listOptions() {
Vector result = new Vector();
result.addElement(new Option(
"\tRepulsion\n" + "\t(default " + m_RepulsionDefault + ")",
"R", 1, "-R "));
return result.elements();
}
/**
* Parses a given list of options.
*
* Valid options are:
*
* -R <num>
* Repulsion
* (default 2.6)
*
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String tmpStr;
tmpStr = Utils.getOption('R', options);
if (tmpStr.length() != 0) {
setRepulsion(Double.parseDouble(tmpStr));
} else {
setRepulsion(m_RepulsionDefault);
}
}
/**
* Gets the current settings of CLOPE
*
* @return an array of strings suitable for passing to setOptions()
*/
public String[] getOptions() {
Vector result;
result = new Vector();
result.add("-R");
result.add("" + getRepulsion());
return (String[]) result.toArray(new String[result.size()]);
}
/**
* Returns the tip text for this property
* @return tip text for this property suitable for
* displaying in the explorer/experimenter gui
*/
public String repulsionTipText() {
return "Repulsion to be used.";
}
/**
* set the repulsion
*
* @param value the repulsion
* @throws Exception if number of clusters is negative
*/
public void setRepulsion(double value) {
m_Repulsion = value;
}
/**
* gets the repulsion
*
* @return the repulsion
*/
public double getRepulsion() {
return m_Repulsion;
}
/**
* Returns default capabilities of the clusterer.
*
* @return the capabilities of this clusterer
*/
public Capabilities getCapabilities() {
Capabilities result = super.getCapabilities();
result.disableAll();
result.enable(Capability.NO_CLASS);
// attributes
result.enable(Capability.NOMINAL_ATTRIBUTES);
// result.enable(Capability.NUMERIC_ATTRIBUTES);
result.enable(Capability.MISSING_VALUES);
return result;
}
/**
* Generate Clustering via CLOPE
* @param instances The instances that need to be clustered
* @throws java.lang.Exception If clustering was not successful
*/
public void buildClusterer(Instances data) throws Exception {
clusters.clear();
m_processed_InstanceID = 0;
m_clusterAssignments.clear();
m_numberOfInstances = data.numInstances();
boolean moved;
//Phase 1
for (int i = 0; i < data.numInstances(); i++) {
int clusterid = AddInstanceToBestCluster(data.instance(i));
m_clusterAssignments.add(clusterid);
}
//Phase 2
do {
moved = false;
for (int i = 0; i < data.numInstances(); i++) {
m_processed_InstanceID = i;
int clusterid = MoveInstanceToBestCluster(data.instance(i));
if (clusterid != m_clusterAssignments.get(i)) {
moved = true;
m_clusterAssignments.set(i, clusterid);
}
}
} while (!moved);
m_processed_InstanceID = 0;
}
/**
* the default constructor
*/
public CLOPE() {
super();
}
/**
* Add instance to best cluster
*/
public int AddInstanceToBestCluster(Instance inst) {
double delta;
double deltamax;
int clustermax = -1;
if (clusters.size() > 0) {
int tempS = 0;
int tempW = 0;
if (inst instanceof SparseInstance) {
for (int i = 0; i < inst.numValues(); i++) {
tempS++;
tempW++;
}
} else {
for (int i = 0; i < inst.numAttributes(); i++) {
if (!inst.isMissing(i)) {
tempS++;
tempW++;
}
}
}
deltamax = tempS / Math.pow(tempW, m_Repulsion);
for (int i = 0; i < clusters.size(); i++) {
CLOPECluster tempcluster = clusters.get(i);
delta = tempcluster.DeltaAdd(inst, m_Repulsion);
// System.out.println("delta " + delta);
if (delta > deltamax) {
deltamax = delta;
clustermax = i;
}
}
} else {
CLOPECluster newcluster = new CLOPECluster();
clusters.add(newcluster);
newcluster.AddInstance(inst);
return clusters.size() - 1;
}
if (clustermax == -1) {
CLOPECluster newcluster = new CLOPECluster();
clusters.add(newcluster);
newcluster.AddInstance(inst);
return clusters.size() - 1;
}
clusters.get(clustermax).AddInstance(inst);
return clustermax;
}
/**
* Move instance to best cluster
*/
public int MoveInstanceToBestCluster(Instance inst) {
clusters.get(m_clusterAssignments.get(m_processed_InstanceID)).DeleteInstance(inst);
m_clusterAssignments.set(m_processed_InstanceID, -1);
double delta;
double deltamax;
int clustermax = -1;
int tempS = 0;
int tempW = 0;
if (inst instanceof SparseInstance) {
for (int i = 0; i < inst.numValues(); i++) {
tempS++;
tempW++;
}
} else {
for (int i = 0; i < inst.numAttributes(); i++) {
if (!inst.isMissing(i)) {
tempS++;
tempW++;
}
}
}
deltamax = tempS / Math.pow(tempW, m_Repulsion);
for (int i = 0; i < clusters.size(); i++) {
CLOPECluster tempcluster = clusters.get(i);
delta = tempcluster.DeltaAdd(inst, m_Repulsion);
// System.out.println("delta " + delta);
if (delta > deltamax) {
deltamax = delta;
clustermax = i;
}
}
if (clustermax == -1) {
CLOPECluster newcluster = new CLOPECluster();
clusters.add(newcluster);
newcluster.AddInstance(inst);
return clusters.size() - 1;
}
clusters.get(clustermax).AddInstance(inst);
return clustermax;
}
/**
* Classifies a given instance.
*
* @param instance The instance to be assigned to a cluster
* @return int The number of the assigned cluster as an integer
* @throws java.lang.Exception If instance could not be clustered
* successfully
*/
public int clusterInstance(Instance instance) throws Exception {
if (m_processed_InstanceID >= m_numberOfInstances) {
m_processed_InstanceID = 0;
}
int i = m_clusterAssignments.get(m_processed_InstanceID);
m_processed_InstanceID++;
return i;
}
/**
* return a string describing this clusterer
*
* @return a description of the clusterer as a string
*/
public String toString() {
StringBuffer stringBuffer = new StringBuffer();
stringBuffer.append("CLOPE clustering results\n" +
"========================================================================================\n\n");
stringBuffer.append("Clustered instances: " + m_clusterAssignments.size() + "\n");
return stringBuffer.toString() + "\n";
}
/**
* Returns a string describing this DataMining-Algorithm
* @return String Information for the gui-explorer
*/
public String globalInfo() {
return getTechnicalInformation().toString();
}
/**
* 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;
result = new TechnicalInformation(Type.INPROCEEDINGS);
result.setValue(Field.AUTHOR, "Yiling Yang and Xudong Guan and Jinyuan You");
result.setValue(Field.TITLE, "CLOPE: a fast and effective clustering algorithm for transactional data");
result.setValue(Field.BOOKTITLE, "Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining");
result.setValue(Field.YEAR, "2002");
result.setValue(Field.PAGES, "682-687");
result.setValue(Field.PUBLISHER, "ACM New York, NY, USA");
return result;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 5538 $");
}
/**
* Main method for testing this class.
*
* @param argv should contain the following arguments:
* -t training file [-R repulsion]
*/
public static void main(String[] argv) {
runClusterer(new CLOPE(), argv);
}
}
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