Java调用weka的各种聚类算法

package com.lh.cluster;


import java.io.BufferedOutputStream;
import java.io.File;
import java.io.FileOutputStream;
import java.io.PrintStream;
import weka.clusterers.EM;
import weka.clusterers.FarthestFirst;
import weka.clusterers.HierarchicalClusterer;
import weka.clusterers.SimpleKMeans;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;


public class ClusterArithmeticAction {
Instances insdata = null;
Instances insdataClusterer  = null;
    String path="";
    int NumCluster=0;
public ClusterArithmeticAction(String filename){
try{
    //获取相对路径--------------------------------------
File directory = new File(".");
    path = directory.getCanonicalPath();       
   //(1)读入样本----------------------------------------
    DataSource source = new DataSource(path+"\\AnswerData\\05"+filename+".arff");
    insdata = source.getDataSet();
       if (insdata.classIndex() == -1)
       insdata.setClassIndex(insdata.numAttributes() - 1);
     //generate data for clusterer (w/o class)
           Remove filter = new Remove();
           filter.setAttributeIndices("" + (insdata.classIndex() + 1));
           filter.setInputFormat(insdata);
           insdataClusterer = Filter.useFilter(insdata, filter);
    NumCluster = Integer.parseInt(filename.substring(filename.indexOf("_")+1));
}catch(Exception ex){
    ex.printStackTrace();
    }
}

//调用SimpleKMeans聚类算法===========================================
public String SimpleKMeansCluster(String filename){
filename = "06"+filename.substring(0,filename.indexOf("_"))+"SC"+filename.substring(filename.indexOf("_"))+".txt";  
try{
//(1)把输出写入记事本
PrintStream ps;        
    FileOutputStream fos = new FileOutputStream(path+"\\AnswerData\\"+filename);
    BufferedOutputStream bos = new BufferedOutputStream(fos, 1024);
    ps = new PrintStream(bos, true);
    System.setOut(ps);
    SimpleKMeans KM = new SimpleKMeans();
    //(2)设置聚类要得到的类别数量 
    KM.setNumClusters(NumCluster);
    //(3)使用聚类算法对样本进行聚类 
    KM.buildClusterer(insdataClusterer); 
    //(4)打印聚类结果
    for(int i=0;i<insdataClusterer.numInstances();i++)
    System.out.println(KM.clusterInstance(insdataClusterer.instance(i)));
    //结束输出流-----
    bos.close();
    fos.close();
    ps.close();
}
catch(Exception ex){
    ex.printStackTrace();
    }
return filename;
}

//调用EM聚类算法===========================================
public String EMCluster(String filename){
filename = "06"+filename.substring(0,filename.indexOf("_"))+"EM"+filename.substring(filename.indexOf("_"))+".txt";  
try{
//(1)把输出写入记事本
PrintStream ps;        
   FileOutputStream fos = new FileOutputStream(path+"\\AnswerData\\"+filename);
   BufferedOutputStream bos = new BufferedOutputStream(fos, 1024);
   ps = new PrintStream(bos, true);
   System.setOut(ps);
   //(2)参数设置----------------------------------------   
   String[] options = weka.core.Utils.splitOptions("-I 100 -N "+ NumCluster+" -M 1.0E-6 -S 100");
    //学习一个EMcluster，比如EM
    EM EMcluster = new EM();           // new instance of EMcluster
    EMcluster.setOptions(options);     // set the options
    EMcluster.buildClusterer(insdataClusterer);    // build the EMcluster   
    //输出评价结果    
    for(int i=0;i<insdataClusterer.numInstances();i++)
    System.out.println(EMcluster.clusterInstance(insdataClusterer.instance(i)));
   //结束输出流-----
   bos.close();
   fos.close();
   ps.close();
}
catch(Exception ex){
   ex.printStackTrace();
}
return filename;
}

//调用FarthestFirst聚类算法===========================================
public String FarthestFirstCluster(String filename){
filename = "06"+filename.substring(0,filename.indexOf("_"))+"FF"+filename.substring(filename.indexOf("_"))+".txt";  
try{
//(1)把输出写入记事本
PrintStream ps;        
   FileOutputStream fos = new FileOutputStream(path+"\\AnswerData\\"+filename);
   BufferedOutputStream bos = new BufferedOutputStream(fos, 1024);
   ps = new PrintStream(bos, true);
   System.setOut(ps);
   //(2)参数设置----------------------------------------   
   String[] options = new String[2];
    options[0] = "-S";                 // max. iterations
    options[1] = "100";
    //学习一个FarthestFirstCluster，比如farthestFirstcluster
    FarthestFirst farthestFirstcluster = new FarthestFirst();           // new instance of EMcluster
    farthestFirstcluster.setOptions(options);     // set the options
    farthestFirstcluster.setNumClusters(NumCluster);
    farthestFirstcluster.buildClusterer(insdataClusterer);    // build the EMcluster   
    //输出评价结果    
    for(int i=0;i<insdataClusterer.numInstances();i++)
    System.out.println(farthestFirstcluster.clusterInstance(insdataClusterer.instance(i)));
   //结束输出流-----
   bos.close();
   fos.close();
   ps.close();
}
catch(Exception ex){
   ex.printStackTrace();
}
return filename;
}

//调用Hierarchical聚类算法===========================================
public String HierarchicalCluster(String filename){
filename = "06"+filename.substring(0,filename.indexOf("_"))+"HC"+filename.substring(filename.indexOf("_"))+".txt";  
try{
//(1)把输出写入记事本
PrintStream ps;        
   FileOutputStream fos = new FileOutputStream(path+"\\AnswerData\\"+filename);
   BufferedOutputStream bos = new BufferedOutputStream(fos, 1024);
   ps = new PrintStream(bos, true);
   System.setOut(ps);
   //(2)参数设置----------------------------------------   
   String[] options = new String[2];
    options[0] = "-L";                 
    options[1] = "WARD";
    //学习一个HierarchicalCluster
    HierarchicalClusterer Hicluster = new HierarchicalClusterer();// new instance of EMcluster
    Hicluster.setOptions(options);     // set the options
    Hicluster.setNumClusters(NumCluster);
    Hicluster.buildClusterer(insdataClusterer);    // build the EMcluster   
    //输出评价结果    
    for(int i=0;i<insdataClusterer.numInstances();i++)
    System.out.println(Hicluster.clusterInstance(insdataClusterer.instance(i)));
   //结束输出流-----
   bos.close();
   fos.close();
   ps.close();
}
catch(Exception ex){
   ex.printStackTrace();
}
return filename;
}





}