最近在网上查看用MapReduce实现的Kmeans算法，例子是不错，http://blog.csdn.net/jshayzf/article/details/22739063

但注释太少了，而且参数太多，如果新手学习的话不太好理解。所以自己按照个人的理解写了一个简单的例子并添加了详细的注释。

大致的步骤是：

1，Map每读取一条数据就与中心做对比，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，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。

3，对比reduce求出的平均值与原来的中心，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）

     删掉reduce的输出目录以便下次输出。

     继续运行任务。

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。

  1 package MyKmeans;
  2 
  3 import java.io.IOException;
  4 import java.util.ArrayList;
  5 
  6 import org.apache.hadoop.conf.Configuration;
  7 import org.apache.hadoop.fs.Path;
  8 import org.apache.hadoop.io.Text;
  9 
 10 import java.util.Arrays;
 11 import java.util.Iterator;
 12 
 13 import org.apache.hadoop.io.IntWritable;
 14 import org.apache.hadoop.io.LongWritable;
 15 import org.apache.hadoop.mapreduce.Job;
 16 import org.apache.hadoop.mapreduce.Mapper;
 17 import org.apache.hadoop.mapreduce.Reducer;
 18 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 19 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 20 
 21 
 22 public class MapReduce {
 23     
 24     public static class Map extends Mapper<LongWritable, Text, IntWritable, Text>{
 25 
 26         //中心集合
 27         ArrayList<ArrayList<Double>> centers = null;
 28         //用k个中心
 29         int k = 0;
 30         
 31         //读取中心
 32         protected void setup(Context context) throws IOException,
 33                 InterruptedException {
 34             centers = Utils.getCentersFromHDFS(context.getConfiguration().get("centersPath"),false);
 35             k = centers.size();
 36         }
 37 
 38 
 39         /**
 40          * 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
 41          * 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)
 42          */
 43         protected void map(LongWritable key, Text value, Context context)
 44                 throws IOException, InterruptedException {
 45             //读取一行数据
 46             ArrayList<Double> fileds = Utils.textToArray(value);
 47             int sizeOfFileds = fileds.size();
 48             
 49             double minDistance = 99999999;
 50             int centerIndex = 0;
 51             
 52             //依次取出k个中心点与当前读取的记录做计算
 53             for(int i=0;i<k;i++){
 54                 double currentDistance = 0;
 55                 for(int j=0;j<sizeOfFileds;j++){
 56                     double centerPoint = Math.abs(centers.get(i).get(j));
 57                     double filed = Math.abs(fileds.get(j));
 58                     currentDistance += Math.pow((centerPoint - filed) / (centerPoint + filed), 2);
 59                 }
 60                 //循环找出距离该记录最接近的中心点的ID
 61                 if(currentDistance<minDistance){
 62                     minDistance = currentDistance;
 63                     centerIndex = i;
 64                 }
 65             }
 66             //以中心点为Key 将记录原样输出
 67             context.write(new IntWritable(centerIndex+1), value);
 68         }
 69         
 70     }
 71     
 72     //利用reduce的归并功能以中心为Key将记录归并到一起
 73     public static class Reduce extends Reducer<IntWritable, Text, Text, Text>{
 74 
 75         /**
 76          * 1.Key为聚类中心的ID value为该中心的记录集合
 77          * 2.计数所有记录元素的平均值，求出新的中心
 78          */
 79         protected void reduce(IntWritable key, Iterable<Text> value,Context context)
 80                 throws IOException, InterruptedException {
 81             ArrayList<ArrayList<Double>> filedsList = new ArrayList<ArrayList<Double>>();
 82             
 83             //依次读取记录集，每行为一个ArrayList<Double>
 84             for(Iterator<Text> it =value.iterator();it.hasNext();){
 85                 ArrayList<Double> tempList = Utils.textToArray(it.next());
 86                 filedsList.add(tempList);
 87             }
 88             
 89             //计算新的中心
 90             //每行的元素个数
 91             int filedSize = filedsList.get(0).size();
 92             double[] avg = new double[filedSize];
 93             for(int i=0;i<filedSize;i++){
 94                 //求没列的平均值
 95                 double sum = 0;
 96                 int size = filedsList.size();
 97                 for(int j=0;j<size;j++){
 98                     sum += filedsList.get(j).get(i);
 99                 }
100                 avg[i] = sum / size;
101             }
102             context.write(new Text("") , new Text(Arrays.toString(avg).replace("[", "").replace("]", "")));
103         }
104         
105     }
106     
107     @SuppressWarnings("deprecation")
108     public static void run(String centerPath,String dataPath,String newCenterPath,boolean runReduce) throws IOException, ClassNotFoundException, InterruptedException{
109         
110         Configuration conf = new Configuration();
111         conf.set("centersPath", centerPath);
112         
113         Job job = new Job(conf, "mykmeans");
114         job.setJarByClass(MapReduce.class);
115         
116         job.setMapperClass(Map.class);
117 
118         job.setMapOutputKeyClass(IntWritable.class);
119         job.setMapOutputValueClass(Text.class);
120 
121         if(runReduce){
122             //最后依次输出不许要reduce
123             job.setReducerClass(Reduce.class);
124             job.setOutputKeyClass(Text.class);
125             job.setOutputValueClass(Text.class);
126         }
127         
128         FileInputFormat.addInputPath(job, new Path(dataPath));
129         
130         FileOutputFormat.setOutputPath(job, new Path(newCenterPath));
131         
132         System.out.println(job.waitForCompletion(true));
133     }
134 
135     public static void main(String[] args) throws ClassNotFoundException, IOException, InterruptedException {
136         String centerPath = "hdfs://localhost:9000/input/centers.txt";
137         String dataPath = "hdfs://localhost:9000/input/wine.txt";
138         String newCenterPath = "hdfs://localhost:9000/out/kmean";
139         
140         int count = 0;
141         
142         
143         while(true){
144             run(centerPath,dataPath,newCenterPath,true);
145             System.out.println(" 第 " + ++count + " 次计算 ");
146             if(Utils.compareCenters(centerPath,newCenterPath )){
147                 run(centerPath,dataPath,newCenterPath,false);
148                 break;
149             }
150         }
151     }
152     
153 }

  1 package MyKmeans;
  2 
  3 import java.io.IOException;
  4 import java.util.ArrayList;
  5 import java.util.List;
  6 
  7 import org.apache.hadoop.conf.Configuration;
  8 import org.apache.hadoop.fs.FSDataInputStream;
  9 import org.apache.hadoop.fs.FSDataOutputStream;
 10 import org.apache.hadoop.fs.FileStatus;
 11 import org.apache.hadoop.fs.FileSystem;
 12 import org.apache.hadoop.fs.Path;
 13 import org.apache.hadoop.io.IOUtils;
 14 import org.apache.hadoop.io.Text;
 15 import org.apache.hadoop.util.LineReader;
 16 
 17 public class Utils {
 18     
 19     //读取中心文件的数据
 20     public static ArrayList<ArrayList<Double>> getCentersFromHDFS(String centersPath,boolean isDirectory) throws IOException{
 21         
 22         ArrayList<ArrayList<Double>> result = new ArrayList<ArrayList<Double>>();
 23         
 24         Path path = new Path(centersPath);
 25         
 26         Configuration conf = new Configuration();
 27         
 28         FileSystem fileSystem = path.getFileSystem(conf);
 29 
 30         if(isDirectory){    
 31             FileStatus[] listFile = fileSystem.listStatus(path);
 32             for (int i = 0; i < listFile.length; i++) {
 33                 result.addAll(getCentersFromHDFS(listFile[i].getPath().toString(),false));
 34             }
 35             return result;
 36         }
 37         
 38         FSDataInputStream fsis = fileSystem.open(path);
 39         LineReader lineReader = new LineReader(fsis, conf);
 40         
 41         Text line = new Text();
 42         
 43         while(lineReader.readLine(line) > 0){
 44             ArrayList<Double> tempList = textToArray(line);
 45             result.add(tempList);
 46         }
 47         lineReader.close();
 48         return result;
 49     }
 50     
 51     //删掉文件
 52     public static void deletePath(String pathStr) throws IOException{
 53         Configuration conf = new Configuration();
 54         Path path = new Path(pathStr);
 55         FileSystem hdfs = path.getFileSystem(conf);
 56         hdfs.delete(path ,true);
 57     }
 58     
 59     public static ArrayList<Double> textToArray(Text text){
 60         ArrayList<Double> list = new ArrayList<Double>();
 61         String[] fileds = text.toString().split(",");
 62         for(int i=0;i<fileds.length;i++){
 63             list.add(Double.parseDouble(fileds[i]));
 64         }
 65         return list;
 66     }
 67     
 68     public static boolean compareCenters(String centerPath,String newPath) throws IOException{
 69         
 70         List<ArrayList<Double>> oldCenters = Utils.getCentersFromHDFS(centerPath,false);
 71         List<ArrayList<Double>> newCenters = Utils.getCentersFromHDFS(newPath,true);
 72         
 73         int size = oldCenters.size();
 74         int fildSize = oldCenters.get(0).size();
 75         double distance = 0;
 76         for(int i=0;i<size;i++){
 77             for(int j=0;j<fildSize;j++){
 78                 double t1 = Math.abs(oldCenters.get(i).get(j));
 79                 double t2 = Math.abs(newCenters.get(i).get(j));
 80                 distance += Math.pow((t1 - t2) / (t1 + t2), 2);
 81             }
 82         }
 83         
 84         if(distance == 0.0){
 85             //删掉新的中心文件以便最后依次归类输出
 86             Utils.deletePath(newPath);
 87             return true;
 88         }else{
 89             //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
 90             
 91             Configuration conf = new Configuration();
 92             Path outPath = new Path(centerPath);
 93             FileSystem fileSystem = outPath.getFileSystem(conf);
 94             
 95             FSDataOutputStream overWrite = fileSystem.create(outPath,true);
 96             overWrite.writeChars("");
 97             overWrite.close();
 98             
 99             
100             Path inPath = new Path(newPath);
101             FileStatus[] listFiles = fileSystem.listStatus(inPath);
102             for (int i = 0; i < listFiles.length; i++) {                
103                 FSDataOutputStream out = fileSystem.create(outPath);
104                 FSDataInputStream in = fileSystem.open(listFiles[i].getPath());
105                 IOUtils.copyBytes(in, out, 4096, true);
106             }
107             //删掉新的中心文件以便第二次任务运行输出
108             Utils.deletePath(newPath);
109         }
110         
111         return false;
112     }
113 }

数据集   http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

运行结果可以与 http://blog.csdn.net/jshayzf/article/details/22739063的结果做对比（前提是初始的中心相同）