# weka 聚类的使用

weka 聚类的使用

1. 流程

   使用loader类的setfile（）方法读入数据

   使用Loader类的getDataSet（）方法得到Instances类的训练集

   new一个Clusterer接口的实现（如EM，SimpleKmeans），使用buildClusterer（Instances ）方法开始训练

   使用Clusterer的clusterInstance（Instance）方法得到instance的分类结果

2. 代码
   这里是对单属性的csv文件进行二分类

   package com.fly.cluster;
   
   import org.jfree.chart.ChartFactory;
   import org.jfree.chart.ChartFrame;
   import org.jfree.chart.JFreeChart;
   import org.jfree.chart.axis.NumberAxis;
   import org.jfree.chart.axis.ValueAxis;
   import org.jfree.chart.plot.PlotOrientation;
   import org.jfree.chart.plot.XYPlot;
   import org.jfree.chart.renderer.xy.XYLineAndShapeRenderer;
   import org.jfree.data.xy.DefaultXYDataset;
   import sun.plugin2.util.ParameterNames;
   import weka.clusterers.Clusterer;
   import weka.clusterers.EM;
   import weka.clusterers.FarthestFirst;
   import weka.clusterers.SimpleKMeans;
   import weka.core.Instance;
   import weka.core.Instances;
   import weka.core.converters.CSVLoader;
   import weka.core.pmml.jaxbbindings.Cluster;
   
   
   import java.awt.*;
   import java.io.BufferedWriter;
   import java.io.File;
   import java.io.FileWriter;
   import java.io.IOException;
   import java.util.ArrayList;
   import java.util.List;
   
   public class Main {
       //kMEANS
       public static SimpleKMeans kMeans(Instances dataSet ) throws Exception {
           SimpleKMeans simpleKMeans = new SimpleKMeans();
           //设置随机数种子
           simpleKMeans.setSeed(20);
           simpleKMeans.setNumClusters(2);
           simpleKMeans.buildClusterer(dataSet);
           return simpleKMeans;
       }
   
       //FarthestFirst
      public  static FarthestFirst farthestFirst(Instances dataSet) throws Exception {
          FarthestFirst first = new FarthestFirst();
          first.setNumClusters(2);
          first.buildClusterer(dataSet);
          return first;
      }
       //EM
       public static EM em(Instances dataSet) throws Exception {
           EM em = new EM();
           em.setNumClusters(2);
           em.buildClusterer(dataSet);
           return em;
       }
   
   
       //输出分类
       public static void output(String path, Clusterer cluster,Instances instances) throws Exception {
           BufferedWriter out = new BufferedWriter(new FileWriter(path));
           out.write("value");
           for (int i=0;i<instances.numInstances();i++){
               out.newLine();
               int i1 = cluster.clusterInstance(instances.instance(i));
               out.write(String.valueOf(i1));
           }
           out.close();
       }
   
       //输出混淆矩阵
       public static void confusionMatrix(Clusterer clusterer,Instances instances,Instances test)throws Exception{
           //这里label 0 等于 value 1
           int TP=0,TN=0,FN=0,FP=0;
           for(int i=0;i<instances.numInstances();i++){
               int value = clusterer.clusterInstance(instances.instance(i));
               double label = test.instance(i).value(0);
   //            if(label==1){
   //                if(value==0)TP++;
   //                else FN++;
   //            }else{
   //                if(value==1)TN++;
   //                else FP++;
   //            }
               if(label==1){
                   if(value==1)TP++;
                   else FN++;
               }else{
                   if(value==0)TN++;
                   else FP++;
               }
           }
           System.out.println(TP+"  "+FN);
           System.out.println(FP+" "+TN);
           int P=TP+FN;
           int N=FP+TN;
           System.out.println("准确率： "+1.0*(TP+TN)/(P+N));
           System.out.println("错误率： "+1.0*(FP+FN)/(P+N));
           System.out.println("召回率： "+1.0*(TP)/(P));
           System.out.println("精度： "+1.0*(TP)/(TP+FP));
       }
   
       //散点图
       public static void show(Clusterer clusterer,Instances instances) throws Exception {
           Integer j=0;
           DefaultXYDataset xydataset = new DefaultXYDataset();
   
           //第一个参数j代表第j组数据，data为2*n大小的double数组
           //xydataset.addSeries(j,data);
           List<Double> a1=new ArrayList<Double>();
           List<Double> a0=new ArrayList<Double>();
           for(int i=0;i<instances.numInstances();i++){
               if(clusterer.clusterInstance(instances.instance(i))==0){
                   a0.add(instances.instance(i).value(0));
               }else {
                   a1.add(instances.instance(i).value(0));
               }
           }
           double[][]d0=new double[2][a0.size()];
           double[][]d1=new double[2][a1.size()];
           for(int i=0;i<a0.size();i++){
               d0[0][i]=a0.get(i);
               d0[1][i]=a0.get(i);
           }
           for(int i=0;i<a1.size();i++){
               d1[0][i]=a1.get(i);
               d1[1][i]=a1.get(i);
           }
           xydataset.addSeries(0,d0);
           xydataset.addSeries(1,d1);
   
   
           JFreeChart chart = ChartFactory.createScatterPlot("分布情况","score","score",xydataset, PlotOrientation.VERTICAL, true, false, false);
   
           ChartFrame frame = new ChartFrame("散点图", chart, true);
           chart.setBackgroundPaint(Color.white);
           chart.setBorderPaint(Color.GREEN);
           chart.setBorderStroke(new BasicStroke(1.5f));
           XYPlot xyplot = (XYPlot) chart.getPlot();
   
   
   
           xyplot.setBackgroundPaint(new Color(255, 253, 246));
           ValueAxis vaaxis = xyplot.getDomainAxis();
           vaaxis.setAxisLineStroke(new BasicStroke(1.5f));
   
           ValueAxis va = xyplot.getDomainAxis(0);
           va.setAxisLineStroke(new BasicStroke(1.5f));
   
   
   
   
   
           va.setAxisLineStroke(new BasicStroke(1.5f)); // 坐标轴粗细
           va.setAxisLinePaint(new Color(215, 215, 215)); // 坐标轴颜色
           xyplot.setOutlineStroke(new BasicStroke(1.5f)); // 边框粗细
           va.setLabelPaint(new Color(10, 10, 10)); // 坐标轴标题颜色
           va.setTickLabelPaint(new Color(102, 102, 102)); // 坐标轴标尺值颜色
           ValueAxis axis = xyplot.getRangeAxis();
           axis.setAxisLineStroke(new BasicStroke(1.5f));
   
           XYLineAndShapeRenderer xylineandshaperenderer = (XYLineAndShapeRenderer) xyplot
                   .getRenderer();
           xylineandshaperenderer.setSeriesOutlinePaint(0, Color.WHITE);
           xylineandshaperenderer.setUseOutlinePaint(true);
           NumberAxis numberaxis = (NumberAxis) xyplot.getDomainAxis();
           numberaxis.setAutoRangeIncludesZero(false);
           numberaxis.setTickMarkInsideLength(2.0F);
           numberaxis.setTickMarkOutsideLength(0.0F);
           numberaxis.setAxisLineStroke(new BasicStroke(1.5f));
   
           //输出中文为方框的解决办法
           Font font=new Font("黑体",Font.BOLD,18);//测试是可以的
           chart.getTitle().setFont(font);
           axis.setLabelFont(font);
           va.setLabelFont(font);
   
           frame.pack();
           frame.setVisible(true);
       }
   
       public static void main(String[] args) throws Exception {
           File file = new File(Main.class.getClassLoader().getResource("test_score.csv").getPath());
           System.out.println(file.exists());
           CSVLoader csvLoader = new CSVLoader();
           csvLoader.setFile(file);
           Instances dataSet = csvLoader.getDataSet();
           Clusterer clusterer=kMeans(dataSet);
   
           //输出评价结果
           String path="D:\\新建文件夹 (2)\\数据挖掘\\论文\\km_20.csv";
           output(path,clusterer,dataSet);
   
   
   
           File test = new File(Main.class.getClassLoader().getResource("labeled.csv").getPath());
           CSVLoader csvLoader1 = new CSVLoader();
           csvLoader1.setFile(test);
           Instances tesetData = csvLoader1.getDataSet();
   
           //混淆矩阵
           confusionMatrix(clusterer,dataSet,tesetData);
   
           //散点图
           show(clusterer,dataSet);
   
           System.out.println(clusterer);
   
   
       }
   }