1、原理解释
(1)原始数据集合List按照一定的规则进行排序，初始距离阈值设置为T1、T2，T1>T2。
(2)在List中随机挑选一个数据向量A，使用一个粗糙距离计算方式计算A与List中其它样本数据向量之间的距离d。
(3)根据2中的距离d，把d小于T1的样本数据向量划到一个canopy中，同时把d小于T2的样本数据向量从List中移除。
(4)重复2、3，直至List为空
2、下载测试数据
cd /tmp
hadoop dfs -mkdir /input
wget http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data
hadoop dfs -copyFromLocal /tmp/synthetic_control.data /input/synthetic_control.data
3、格式转换（文本→向量）
编辑文件 Text2VectorWritable.jar
package mahout.fansy.utils.transform;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.SequenceFileOutputFormat;
import org.apache.hadoop.util.ToolRunner;
import org.apache.mahout.common.AbstractJob;
import org.apache.mahout.math.RandomAccessSparseVector;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.VectorWritable;
/**
* --* transform text data to vectorWritable data
* --* @author fansy
* --*
* --*/
public class Text2VectorWritable extends AbstractJob{
public static void main(String[] args) throws Exception{
ToolRunner.run(new Configuration(), new Text2VectorWritable(),args);
}
@Override
public int run(String[] arg0) throws Exception {
addInputOption();
addOutputOption();
if (parseArguments(arg0) == null) {
return -1;
}
Path input=getInputPath();
Path output=getOutputPath();
Configuration conf=getConf();
// set job information
Job job=new Job(conf,"text2vectorWritableCopy with input:"+input.getName());
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setMapperClass(Text2VectorWritableMapper.class);
job.setMapOutputKeyClass(LongWritable.class);
job.setMapOutputValueClass(VectorWritable.class);
job.setReducerClass(Text2VectorWritableReducer.class);
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(VectorWritable.class);
job.setJarByClass(Text2VectorWritable.class);
FileInputFormat.addInputPath(job, input);
SequenceFileOutputFormat.setOutputPath(job, output);
if (!job.waitForCompletion(true)) { // wait for the job is done
throw new InterruptedException("Canopy Job failed processing " + input);
}
return 0;
}
/**
* Mapper main procedure
* @author fansy
*
--*/
public static class Text2VectorWritableMapper extends Mapper
public void map(LongWritable key,Text value,Context context) throws IOException,InterruptedException{
String[] str=value.toString().split("\\s{1,}");
// split data use one or more blanker
Vector vector=new RandomAccessSparseVector(str.length);
for(int i=0;i vector.set(i, Double.parseDouble(str[i])); } VectorWritable va=new VectorWritable(vector); context.write(key, va); } } /** * Reducer: do nothing but output * @author fansy * --*/ public static class Text2VectorWritableReducer extends Reducer public void reduce(LongWritable key,Iterable for(VectorWritable v:values){ context.write(key, v); } } } } 编译，输出ClusteringUtils.jar，并拷贝至/home/mahout/mahout_jar 输出时选择Export→Runnable Jar File→Extract required libraries into generated JAR 然后执行： hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar mahout.fansy.utils.transform.Text2VectorWritable -i hdfs:///input/synthetic_control.data -o hdfs:///input/synthetic_control.data.transform 有可能会遇到org/apache/mahout/common/AbstractJob找不到类报错，这个一般是由于HADOOP_CLASSPATH配置位置不包含mahout的jar的原因。 解决方法1： 拷贝mahout的jar文件到/home/hadoop/lib中去，并确认这个/home/hadoop/lib确实在HADOOP_CLASSPATH中 cp /home/hadoop/mahout/*.jar /home/hadoop/hadoop/lib 解决方法2（推荐）： 在hadoop-env.sh中加入 for f in /home/hadoop/mahout/*.jar; do if [ "$HADOOP_CLASSPATH" ]; then export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$f else export HADOOP_CLASSPATH=$f fi done 记得将hadoop-evn.sh分发到其它节点 重启hadoop环境 stop-all.sh start-all.sh 执行转换： hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar mahout.fansy.utils.transform.Text2VectorWritable -i hdfs:///input/synthetic_control.data -o hdfs:///input/synthetic_control.data.transform（如果在导出Jar的时候已经指派主类，这个命令会报错，使用下面的命令） hadoop jar /home/hadoop/mahout/mahout_jar/ClusteringUtils.jar -o hdfs:///input/synthetic_control.data.transform 输出完毕的文件已经是面目全非的Vector文件了 hdfs:///input/synthetic_control.data.transform/part-r-00000 4、执行Canopy聚类 mahout canopy --input hdfs:///input/synthetic_control.data.transform/part-r-00000 --output /output/canopy --distanceMeasure org.apache.mahout.common.distance.EuclideanDistanceMeasure --t1 80 --t2 55 --t3 80 --t4 55 --clustering 5、转换格式（向量→文本） 把4中的结果转换成为文本 编辑文件ReadClusterWritable.java package mahout.fansy.utils; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.ToolRunner; import org.apache.mahout.clustering.iterator.ClusterWritable; import org.apache.mahout.common.AbstractJob; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** ??* read cluster centers ??* @author fansy ??*/ public class ReadClusterWritable extends AbstractJob { public static void main(String[] args) throws Exception{ ToolRunner.run(new Configuration(), new ReadClusterWritable(),args); } @Override public int run(String[] args) throws Exception { addInputOption(); addOutputOption(); if (parseArguments(args) == null) { return -1; } Job job=new Job(getConf(),getInputPath().toString()); job.setInputFormatClass(SequenceFileInputFormat.class); job.setMapperClass(RM.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(Text.class); job.setNumReduceTasks(0); job.setJarByClass(ReadClusterWritable.class); FileInputFormat.addInputPath(job, getInputPath()); FileOutputFormat.setOutputPath(job, getOutputPath()); if (!job.waitForCompletion(true)) { throw new InterruptedException("Canopy Job failed processing " + getInputPath()); } return 0; } public static class RM extends Mapper private Logger log=LoggerFactory.getLogger(RM.class); public void map(Text key,ClusterWritable value,Context context) throws IOException,InterruptedException{ String str=value.getValue().getCenter().asFormatString(); // System.out.println("center****************:"+str); log.info("center*****************************:"+str); // set log information context.write(key, new Text(str)); } } } 打包到ClusteringUtils.jar，上传到/home/hadoop/mahout/mahout_jar 如果需要清除eclipse中Launch Configuration中的信息，需要进入工程所在文件夹下的/.metadata/.plugins/org.eclipse.debug.core/.launches 然后删除里面的文件 运行 hadoop jar ClusteringUtils.jar mahout.fansy.utils.ReadClusterWritable -i /output/canopy/clusters-0-final/part-r-00000 -o /output/canopy-output（如果不成功就运行下面的命令） hadoop jar ClusteringUtils.jar -i /output/canopy/clusters-0-final/part-r-00000 -o /output/canopy-output 这时候/output/canopy-output/part-m-00000里面放置的就是聚类的结果文件