K-Means算法的Hadoop实现，k-means算法hadoop
一、MapReduce实现K-Means算法的基本流程
1、Map每读取一条数据就与所有选定的中心做对比，求出该条记录对应的中心，然后以中心的ID为Key，该条数据为value将数据输出。
2，利用reduce的归并功能将相同的Key归并到一起（因为map把中心的ID作为key，所以在reduce端时数据已经按照各自的中心分好了组，这是MapReduce框架自动完成的），集中与该Key对应的数据，再求出这些数据的平均值，输出平均值，以便进行新的中心的选取。
3，对比reduce求出的平均值与原来或者上一次的中心，如果不相同，这将清空原中心的数据文件，将reduce的结果写到中心文件中。（中心的值存在一个HDFS的文件中）同时删掉reduce的输出目录以便下次输出。然后继续循环1、2、3步骤。
4，对比reduce求出的平均值与原来的中心，如果相同。则删掉reduce的输出目录，运行一个没有reduce的任务将中心ID与值对应输出。
二、代码演示
1、辅助工具类
package com.kang.kmeans;
import java.io.IOException;
import java.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FSDataOutputStream;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IOUtils;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.util.LineReader;
public class Utils {
//读取中心文件的数据
public static ArrayList<ArrayList<Double>> getCentersFromHDFS(String centersPath,boolean isDirectory) throws IOException{
ArrayList<ArrayList<Double>> result = new ArrayList<ArrayList<Double>>();
Path path = new Path(centersPath);
Configuration conf = new Configuration();
FileSystem fileSystem = path.getFileSystem(conf);
if(isDirectory){
FileStatus[] listFile = fileSystem.listStatus(path);
for (int i = 0; i < listFile.length; i++) {
result.addAll(getCentersFromHDFS(listFile[i].getPath().toString(),false));
}
return result;
}
FSDataInputStream fsis = fileSystem.open(path);
LineReader lineReader = new LineReader(fsis, conf);
Text line = new Text();
while(lineReader.readLine(line) > 0){
ArrayList<Double> tempList = textToArray(line);
result.add(tempList);
}
lineReader.close();
return result;
}
//删掉文件
public static void deletePath(String pathStr) throws IOException{
Configuration conf = new Configuration();
Path path = new Path(pathStr);
FileSystem hdfs = path.getFileSystem(conf);
hdfs.delete(path ,true);
}
//文本string类型转为数组类型
public static ArrayList<Double> textToArray(Text text){
ArrayList<Double> list = new ArrayList<Double>();
String[] fileds = text.toString().split(",");
for(int i=0;i<fileds.length;i++){
list.add(Double.parseDouble(fileds[i]));
}
return list;
}
//比较新旧中心点的变化情况
public static boolean compareCenters(String centerPath,String newPath) throws IOException{
List<ArrayList<Double>> oldCenters = Utils.getCentersFromHDFS(centerPath,false);
List<ArrayList<Double>> newCenters = Utils.getCentersFromHDFS(newPath,true);
int size = oldCenters.size();
int fildSize = oldCenters.get(0).size();
double distance = 0;
for(int i=0;i<size;i++){
for(int j=1;j<fildSize;j++){
double t1 = Math.abs(oldCenters.get(i).get(j));
double t2 = Math.abs(newCenters.get(i).get(j));
distance += Math.pow((t1 - t2) / (t1 + t2), 2);//根据具体情况选择相应的距离算法，这里简化处理
}
}
if(distance == 0.0){
//删掉新的中心文件以便最后依次归类输出
Utils.deletePath(newPath);
return true;
}else{
//先清空中心文件，将新的中心文件复制到中心文件中，再删掉中心文件
Configuration conf = new Configuration();
Path outPath = new Path(centerPath);
FileSystem fileSystem = outPath.getFileSystem(conf);
FSDataOutputStream overWrite = fileSystem.create(outPath,true);
overWrite.writeChars("");
overWrite.close();
Path inPath = new Path(newPath);
FileStatus[] listFiles = fileSystem.listStatus(inPath);
for (int i = 0; i < listFiles.length; i++) {
FSDataOutputStream out = fileSystem.create(outPath);
FSDataInputStream in = fileSystem.open(listFiles[i].getPath());
IOUtils.copyBytes(in, out, 4096, true);
}
//删掉新的中心文件以便第二次任务运行输出
Utils.deletePath(newPath);
}
return false;
}
}
2、MapReduce任务代码
package com.kang.kmeans;
import java.io.IOException;
import java.util.ArrayList;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import java.util.Arrays;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class MapReduce {
public static class Map extends Mapper<LongWritable, Text, IntWritable, Text>{
//中心集合
ArrayList<ArrayList<Double>> centers = null;
//用k个中心
int k = 0;
//读取中心
protected void setup(Context context) throws IOException,
InterruptedException {
centers = Utils.getCentersFromHDFS(context.getConfiguration().get("centersPath"),false);
k = centers.size();//获取中心点个数
}
/**
* 1.每次读取一条要分类的条记录与中心做对比，归类到对应的中心
* 2.以中心ID为key，中心包含的记录为value输出(例如： 1 0.2---->1为聚类中心的ID，0.2为靠近聚类中心的某个值)
*/
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
//读取一行数据
ArrayList<Double> fileds = Utils.textToArray(value);
int sizeOfFileds = fileds.size();
double minDistance = 99999999;
int centerIndex = 0;
//依次取出k个中心点与当前读取的记录做计算
for(int i=0;i<k;i++){
double currentDistance = 0;
for(int j=1;j<sizeOfFileds;j++){
double centerPoint = Math.abs(centers.get(i).get(j));
double filed = Math.abs(fileds.get(j));
currentDistance += Math.pow((centerPoint - filed) / (centerPoint + filed), 2);//这里的距离算法简化处理
}
//循环找出距离该记录最接近的中心点的ID
if(currentDistance<minDistance){
minDistance = currentDistance;
centerIndex = i;
}
}
//以中心点为Key 将记录原样输出
context.write(new IntWritable(centerIndex+1), value);
}
}
//利用reduce的归并功能以中心为Key将记录归并到一起
public static class Reduce extends Reducer<IntWritable, Text, Text, Text>{
/**
* 1.Key为聚类中心的ID value为该中心的记录集合
* 2.计数所有记录元素的平均值，求出新的中心
*/
protected void reduce(IntWritable key, Iterable<Text> value,Context context)
throws IOException, InterruptedException {
ArrayList<ArrayList<Double>> filedsList = new ArrayList<ArrayList<Double>>();
//依次读取记录集，每行为一个ArrayList<Double>
for(Iterator<Text> it =value.iterator();it.hasNext();){
ArrayList<Double> tempList = Utils.textToArray(it.next());
filedsList.add(tempList);
}
//计算新的中心
//每行的元素个数
int filedSize = filedsList.get(0).size();
double[] avg = new double[filedSize];
for(int i=1;i<filedSize;i++){
//求没列的平均值
double sum = 0;
int size = filedsList.size();
for(int j=0;j<size;j++){
sum += filedsList.get(j).get(i);
}
avg[i] = sum / size;
}
context.write(new Text("") , new Text(Arrays.toString(avg).replace("[", "").replace("]", "")));
}
}
@SuppressWarnings("deprecation")
public static void run(String centerPath,String dataPath,String newCenterPath,boolean runReduce) throws IOException, ClassNotFoundException, InterruptedException{
Configuration conf = new Configuration();
conf.set("centersPath", centerPath);
Job job = new Job(conf, "mykmeans");
job.setJarByClass(MapReduce.class);
job.setMapperClass(Map.class);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(Text.class);
if(runReduce){
//最后依次输出不需要reduce
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
}
FileInputFormat.addInputPath(job, new Path(dataPath));
FileOutputFormat.setOutputPath(job, new Path(newCenterPath));
System.out.println(job.waitForCompletion(true));
}
public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException {
String centerPath = "hdfs://sparkproject1:9000/input/centers.txt";//存放中心点坐标值
String dataPath = "hdfs://sparkproject1:9000/input/wine.txt";//存放待处理数据
String newCenterPath = "hdfs://sparkproject1:9000/out/kmean";//结果输出目录
int count = 0;
while(true){
run(centerPath,dataPath,newCenterPath,true);
System.out.println(" 第 " + ++count + " 次计算 ");
if(Utils.compareCenters(centerPath,newCenterPath )){
run(centerPath,dataPath,newCenterPath,false);
break;
}
}
}
}
3、输入数据
输入数据格式如下：
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
注意每条记录的第一个数据表示其所在的组别序号，在运算时不作为数据参与计算。
完整数据链接如下：wine.data
4、测试结果
- 控制台显示
- 聚类结果显示、
详细结果如下：
1 1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
1 1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
1 1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
1 1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
1 1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
1 1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
1 1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
1 1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
1 1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
1 1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
1 1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
1 1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
1 2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
1 1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
1 1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
1 2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
1 1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
1 1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
1 1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
1 1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
1 1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
1 1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
1 1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
1 1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
1 1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
1 1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
1 1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
1 1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
1 1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
1 1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
1 1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
1 1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
1 1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
1 1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
1 1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
1 1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
1 2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
1 2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
1 1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
1 1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
1 1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
1 1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
1 1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
1 2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
1 2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
1 2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
1 1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
1 2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
1 1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
1 1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
1 1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
1 1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
1 1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
1 1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
1 1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
1 1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
1 1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
1 1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
1 1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
1 1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
1 1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
1 1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
1 1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
1 1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
1 1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
1 1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
1 1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
2 2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
2 3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
2 2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
2 2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
2 2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
2 2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
2 2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
2 2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
2 2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
2 2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
2 2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
2 2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
2 2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
2 2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
2 2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
2 2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
2 2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
2 2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
2 2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
2 2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
2 2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
2 2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
2 2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
2 2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
2 2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
2 2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
2 2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
2 2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
2 2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
2 2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
2 2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
2 2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
2 2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
2 2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
2 2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
2 2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
2 2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
2 2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
2 2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
2 2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
2 2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
2 2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
2 2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
2 2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
2 2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
2 2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
2 2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
2 2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
2 2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
2 2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
2 2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
2 2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
2 2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
2 2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
2 2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
2 2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
2 2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
2 2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
2 2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
2 2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
2 2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
3 2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
3 2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
3 2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
3 3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
3 3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
3 3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
3 3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
3 3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
3 3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
3 3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
3 3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
3 3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
3 3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
3 3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
3 3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
3 3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
3 3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
3 3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
3 3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
3 3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
3 3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
3 3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
3 3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
3 3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
3 3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
3 3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
3 3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
3 3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
3 3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
3 3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
3 3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
3 3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
3 3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
3 3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
3 3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
3 3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
3 3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
3 3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
3 3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
3 3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
3 3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
3 3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
3 3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
3 3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
3 3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
3 3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
3 3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
3 3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
3 3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
3 3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
- 最终的聚类中心
详细结果如下：
0.0, 13.584179104477613, 1.9310447761194038, 2.4437313432835817, 17.526865671641794, 106.65671641791045, 2.8708955223880586, 3.0002985074626864, 0.28805970149253735, 1.9253731343283582, 5.375820895522389, 1.0689552238805968, 3.1400000000000006, 1065.0746268656717
0.0, 12.245737704918037, 1.953606557377049, 2.2381967213114753, 20.17213114754098, 93.40983606557377, 2.1608196721311472, 1.9619672131147534, 0.36508196721311476, 1.584918032786886, 2.8736065573770495, 1.0537049180327864, 2.774754098360654, 502.37704918032784
0.0, 13.1396, 3.3464000000000005, 2.4195999999999995, 21.305999999999997, 98.2, 1.6874, 0.8101999999999998, 0.45679999999999993, 1.15, 7.29739998, 0.6906000000000002, 1.7047999999999994, 618.84
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