PROSAGA码农传奇-hadoop其他-Mahout聚类 - 单个聚类中的所有文本向量

<div class =“post-text”itemprop =“text”>
  <P>
    正如Anony-Mousse在第一次回复中所说，我提供的数据属于一个集群。在最近几周进行了一些灵魂搜索之后（或者更具体地说，直接尝试了距离测量类），我找到了一个导致多个集群的数据集：
  </p>
  <H1>
    1）确保数据足够不同
  </H1>
   <pre>
    <code>
      Text id1 = new Text("Document 1");
Text text1 = new Text("Atletico Madrid win");
writer.append(id1, text1);

Text id6 = new Text("Document 6");
Text text6 = new Text("Both apple and orange are fruit");
writer.append(id6, text6);

Text id7 = new Text("Document 7");
Text text7 = new Text("Both orange and apple are fruit");
writer.append(id7, text7);

</code>
  </pre>
  <H1>
    2）确定良好的半径值
  </H1>
  <H2>
    a）使用您的样本数据试验DistanceMeasure类
  </H2>
   <pre>
    <code>
      Vector v1 = toVector("Atletico Madrid win");
Vector v2 = toVector("Both apple and orange are fruit");
Vector v3 = toVector("Both orange and apple are fruit");
of = ImmutableList.of(v1, v2, v3);

List<Vector> vectorList = new LinkedList();
vectorList.addAll(of);
List<Canopy> canopies = CanopyClusterer.createCanopies(vectorList, new CosineDistanceMeasure(), 0.3, 0.3);
for (Canopy canopy : canopies) {
    System.out.println("DistanceMeasureMain.main() " + canopy.asFormatString());
}

</code>
  </pre>
  <P>
    生产：
  </p>
   <pre>
    <code>
      DistanceMeasureMain.main() distance is 0.19193857965451055
DistanceMeasureMain.main() distance is 0.5281191379648771
DistanceMeasureMain.main() distance is 0.19193857965451055
DistanceMeasureMain.main() C0: {0:1.1,117724:1.0,378550445:1.0,1997849123:1.0}
DistanceMeasureMain.main() C1: {0:1.1,96727:1.0,96852:1.0,2076577:1.0,93029210:1.0,97711124:1.0,1008851410:1.0}

</code>
  </pre>
  <H2>
    b）使用距离作为半径值
  </H2>
  <P>
    我觉得
     <code>
      t1
    </code>
     和
     <code>
      t2
    </code>
     价值观（
     <code>
      0.2
    </code>
     和
     <code>
      0.2
    </code>
    ）为
     <code>
      CanopyDriver.run()
    </code>
     虽然我不知道复杂细节中所有数值参数在下面的调用中的效果，但也很重要：
  </p>
   <pre>
    <code>
          // CosineDistanceMeasure
    CanopyDriver.run(new Path(vectorsFolder),
            new Path(canopyCentroids), new CosineDistanceMeasure(),
            0.2, 0.2, true, 1, true);

FuzzyKMeansDriver.run(new Path(vectorsFolder), new Path(
            canopyCentroids, "clusters-0-final"), new Path(
            clusterOutput), 0.01, 20, 2, true, true, 0, false);

</code>
  </pre>
  <H1>
    产量
  </H1>
   <pre>
    <code>
      Document 1 -> Atletico Madrid win
Document 6 -> Both apple and orange are fruit
Document 7 -> Both orange and apple are fruit

Clusters: 
         0 -> wt: 1.0 distance: 0.0  vec: Document 1 = [1:1.405, 4:1.405, 6:1.405]
         1 -> wt: 1.0 distance: 0.0  vec: Document 6 = [0:1.000, 2:1.000, 3:1.000, 5:1.000]
         1 -> wt: 1.0 distance: 0.0  vec: Document 7 = [0:1.000, 2:1.000, 3:1.000, 5:1.000]

</code>
  </pre>
</DIV>

Mahout聚类 - 单个聚类中的所有文本向量 - 为什么？