Mahout 0.9中的聚类(Clustering)工具的用法

1164 Mahout 工具

问答对人有帮助，内容完整，我也想知道答案 0 什么是Mahout？怎样去使用Mahout 0.9中的聚类(Clustering)工具？ 0
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什么是Mahout？
” Apache Mahout™ project’s goal is to build a scalable machine learning library ”
我来拓展一下：
(1) Mahout 是Apache旗下的开源项目，集成了大量的机器学习算法。
(2) 大部分算法，可以运行在Hadoop上，具有很好的拓展性，使得大数据上的机器学习成为可能。
本篇主要探讨 Mahout 0.9 中的聚类(Clustering)工具的用法。
一、数据准备
Mahout聚类算法的输入为List，即需要将每个待聚类的文档，表示为向量形式。
在本文中，我们选择经典的 Reuters21578 文本语料。尝试对新闻内容进行文本聚类。
1、下载数据
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[color=#002D7A !important]axel [color=#006FE0 !important][color=#006FE0 !important]- n [color=#006FE0 !important][color=#CE0000 !important]20 [color=#006FE0 !important][color=#002D7A !important]http [color=#006FE0 !important]: [color=#FF8000 !important]//kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz

2、解压缩数据
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[color=#002D7A !important]tar [color=#006FE0 !important][color=#006FE0 !important]- xzvf [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters21578 [color=#333333 !important]. [color=#002D7A !important]tar [color=#333333 !important]. gz [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sgm

解压缩之后，reuters-sgm下，包含了若干*.sgm文件，每个文件中又包含了若干下属结构化文档：
[color=#333333 !important]

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[color=#006FE0 !important]< [color=#002D7A !important]DATE [color=#006FE0 !important]> [color=#CE0000 !important]26 [color=#006FE0 !important]- [color=#002D7A !important]FEB [color=#006FE0 !important]- [color=#CE0000 !important]1987 [color=#006FE0 !important][color=#CE0000 !important]15 [color=#006FE0 !important]: [color=#CE0000 !important]02 [color=#006FE0 !important]: [color=#CE0000 !important]20.00 [color=#006FE0 !important]< [color=#006FE0 !important]/ [color=#002D7A !important]DATE [color=#006FE0 !important]>
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[color=#006FE0 !important]< [color=#002D7A !important]DATELINE [color=#006FE0 !important]> [color=#006FE0 !important] [color=#002D7A !important]CLEVELAND [color=#333333 !important], [color=#006FE0 !important]Feb [color=#006FE0 !important][color=#CE0000 !important]26 [color=#006FE0 !important][color=#006FE0 !important]- [color=#006FE0 !important][color=#006FE0 !important]< [color=#006FE0 !important]/ [color=#002D7A !important]DATELINE [color=#006FE0 !important]> [color=#006FE0 !important]< [color=#002D7A !important]BODY [color=#006FE0 !important]> [color=#004ED0 !important]Standard [color=#004ED0 !important]Oil [color=#004ED0 !important]Co [color=#800080 !important]and [color=#006FE0 !important][color=#004ED0 !important]BP [color=#004ED0 !important]North [color=#004ED0 !important]America
[color=#004ED0 !important]Inc [color=#004ED0 !important]said [color=#004ED0 !important]they [color=#004ED0 !important]plan [color=#800080 !important]to [color=#006FE0 !important]form [color=#006FE0 !important]a [color=#006FE0 !important][color=#004ED0 !important]venture [color=#800080 !important]to [color=#006FE0 !important][color=#004ED0 !important]manage [color=#004ED0 !important]the [color=#004ED0 !important]money [color=#004ED0 !important]market
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[color=#006FE0 !important]Reuter
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[color=#006FE0 !important]< [color=#006FE0 !important]/ [color=#002D7A !important]REUTERS [color=#006FE0 !important]>

在下文中，我们主要使用和<BODY>中的文本。即标题+正文。 3、抽取 Mahout中内置了对上述Reuters预料的抽取程序，我们可以直接使用。 [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]mahout [color=#002D7A !important]org [color=#333333 !important]. [color=#002D7A !important]apache [color=#333333 !important]. [color=#002D7A !important]lucene [color=#333333 !important]. [color=#002D7A !important]benchmark [color=#333333 !important]. [color=#002D7A !important]utils [color=#333333 !important]. ExtractReuters [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- sgm [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]out </td></tr></table>如上所述，抽取好的结果在./reuters-out文件夹下面，每篇<REUTERS>文档，变成了一个独立的文件。 一共有21578个txt，即数据集中含有21578篇文档：-） 说下命名规则吧，例如：文件名：./reuters-out/reut2-006.sgm-246.txt，表示来自于./reuters-sgm/reut2-006.sgm中的第246篇文档，下标从0开始。 4、转换成SequenceFile 对于传统的文本聚类算法而言，下一步应该是：将文本转化为词的向量空间表示。 然而，不要太着急哦。 由于Mahout运行在Hadoop上，HDFS是为大文件设计的。如果我们把上述21578个txt都拷贝上去，这样是非常不合适的 设想下：假设对1000万篇新闻进行聚类，难道要拷贝1000w个文件么？这会把name node搞挂的。 因此，Mahout采用SequenceFile作为其基本的数据交换格式。 内置的seqdirectory命令(这个命令设计的不合理，应该叫directoryseq才对)，可以完成文本目录->SequenceFile的转换过程。 [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]mahout [color=#002D7A !important]seqdirectory [color=#006FE0 !important][color=#006FE0 !important]- i [color=#006FE0 !important][color=#002D7A !important]file [color=#006FE0 !important]: [color=#FF8000 !important]//$(pwd)/reuters-out/ -o file://$(pwd)/reuters-seq/ -c UTF-8 -chunk 64 -xm sequential </td></tr></table>上述命令蕴含了2个大坑，在其他文档中均没有仔细说明： (1) -xm sequential，表示在本地执行，而不是用MapReduce执行。如果是后者，我们势必要将这些小文件上传到HDFS上，那样的话，还要SequenceFile做甚…… (2) 然而seqdirectory在执行的时候，并不因为十本地模式，就在本地文件系统上寻找。而是根据-i -o的文件系统前缀来判断文件位置。也就是说，默认情况，依然十在HDFS上查找的……所以，这个file://的前缀是非常有必要的。 其他2个参数： <ul> <li>-c UTF8：编码。 <li>-chunk 64：64MB一个Chunk，应该和HDFS的BLOCK保持一致或者倍数关系。 </ul>5、转换为向量表示 为了适应多种数据，聚类算法多使用向量空间作为输入数据。 由于我们先前已经得到了处理好的SequenceFile，从这一步开始，就可以在Hadoop上进行啦。 [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#004ED0 !important]put [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]seq [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 </td></tr></table>开始text->Vector的转换： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]mahout [color=#002D7A !important]seq2sparse [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]i [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]seq [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]o [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]ow [color=#006FE0 !important][color=#006FE0 !important]-- [color=#004ED0 !important]weight [color=#002D7A !important]tfidf [color=#006FE0 !important][color=#006FE0 !important]-- maxDFPercent [color=#006FE0 !important][color=#CE0000 !important]85 [color=#006FE0 !important][color=#006FE0 !important]-- [color=#002D7A !important]namedVector </td></tr></table>输入和输出不解释了。在Mahout中的向量类型可以称为sparse。 参数说明如下： <ul> <li>-ow( 或 –overwrite)：即使输出目录存在，依然覆盖。 <li>–weight(或 -wt) tfidf：权重公式，大家都懂的。其他可选的有tf (当LDA时建议使用)。 <li>–maxDFPercent(或 -x) 85：过滤高频词，当DF大于85%时，将不在作为词特征输出到向量中。 <li>–namedVector (或-nv)：向量会输出附加信息。 </ul>其他可能有用的选项： <ul> <li>–analyzerName(或-a)：指定其他分词器。 <li>–minDF：最小DF阈值。 <li>–minSupport：最小的支持度阈值，默认为2。 <li>–maxNGramSize(或-ng)：是否创建ngram，默认为1。建议一般设定到2就够了。 <li>–minLLR(或 -ml)：The minimum Log Likelihood Ratio。默认为1.0。当设定了-ng > 1后，建议设置为较大的值，只过滤有意义的N-Gram。 <li>–logNormalize(或 -lnorm)：是否对输出向量做Log变换。 <li>–norm(或 -n)：是否对输出向量做p-norm变换，默认不变换。 </ul>看一下产出： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 2 3 4 5 6 7 8 9 </td><td width="623">[color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]ls [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#004ED0 !important]sparse Found [color=#006FE0 !important][color=#CE0000 !important]7 [color=#006FE0 !important][color=#002D7A !important]items [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]df [color=#006FE0 !important]- [color=#002D7A !important]count [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]dictionary [color=#333333 !important]. [color=#002D7A !important]file [color=#006FE0 !important]- [color=#CE0000 !important]0 [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]frequency [color=#333333 !important]. [color=#002D7A !important]file [color=#006FE0 !important]- [color=#CE0000 !important]0 [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]tf [color=#006FE0 !important]- [color=#002D7A !important]vectors [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]tfidf [color=#006FE0 !important]- [color=#002D7A !important]vectors [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]tokenized [color=#006FE0 !important]- [color=#002D7A !important]documents [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]wordcount </td></tr></table>说明各个文件的用途： <ul> <li>dictionary.file-0：词文本 -> 词id(int)的映射。词转化为id，这是常见做法。 <li>frequency.file：词id -> 文档集词频(cf)。 <li>wordcount(目录)：词文本 -> 文档集词频(cf)，这个应该是各种过滤处理之前的信息。 <li>df-count(目录)：词id -> 文档频率(df)。 <li>tf-vectors、tfidf-vectors (均为目录)：词向量，每篇文档一行，格式为{词id:特征值}，其中特征值为tf或tfidf。有用采用了内置类型VectorWritable，需要用命令”mahout vectordump -i <path>”查看。 <li>tokenized-documents：分词后的文档。 </ul>二、KMeans 1、运行K-Means [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]mahout [color=#002D7A !important]kmeans [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]i [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]tfidf [color=#006FE0 !important]- [color=#002D7A !important]vectors [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]c [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]- [color=#002D7A !important]clusters [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]o [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important][color=#006FE0 !important]- k [color=#006FE0 !important][color=#CE0000 !important]20 [color=#006FE0 !important][color=#006FE0 !important]- [color=#004ED0 !important]dm [color=#002D7A !important]org [color=#333333 !important]. [color=#002D7A !important]apache [color=#333333 !important]. [color=#002D7A !important]mahout [color=#333333 !important]. [color=#002D7A !important]common [color=#333333 !important]. [color=#002D7A !important]distance [color=#333333 !important]. [color=#002D7A !important]CosineDistanceMeasure [color=#006FE0 !important][color=#006FE0 !important]- x [color=#006FE0 !important][color=#CE0000 !important]200 [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]ow [color=#006FE0 !important][color=#006FE0 !important]-- [color=#002D7A !important]clustering </td></tr></table>参数说明如下： <ul> <li>-i：输入为上面产出的tfidf向量。 <li>-o：每一轮迭代的结果将输出在这里。 <li>-k：几个簇。 <li>-c：这是一个神奇的变量。若不设定k，则用这个目录里面的点，作为聚类中心点。否则，随机选择k个点，作为中心点。 <li>-dm：距离公式，文本类型推荐用cosine距离。 <li>-x ：最大迭代次数。 <li>–clustering：在mapreduce模式运行。 <li>–convergenceDelta：迭代收敛阈值，默认0.5，对于Cosine来说略大。 </ul>输出1，初始随机选择的中心点： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 2 3 </td><td width="623">[color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]ls [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]- [color=#004ED0 !important]clusters Found [color=#006FE0 !important][color=#CE0000 !important]1 [color=#006FE0 !important][color=#002D7A !important]items [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]- [color=#002D7A !important]clusters [color=#006FE0 !important]/ [color=#002D7A !important]part [color=#006FE0 !important]- [color=#002D7A !important]randomSeed </td></tr></table>输出2，聚类过程、结果： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 2 3 4 5 6 7 </td><td width="623">[color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]ls [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#004ED0 !important]kmeans Found [color=#006FE0 !important][color=#CE0000 !important]5 [color=#006FE0 !important][color=#002D7A !important]items [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]_policy [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusteredPoints [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusters [color=#006FE0 !important]- [color=#CE0000 !important]0 [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusters [color=#006FE0 !important]- [color=#CE0000 !important]1 [color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusters [color=#006FE0 !important]- [color=#CE0000 !important]2 [color=#006FE0 !important]- [color=#800080 !important]final </td></tr></table>其中，clusters-k(-final)为每次迭代后，簇的20个中心点的信息。 而clusterdPoints，存储了簇id -> 文档id 的映射。 2、查看簇结果 首先，用clusterdump，来查看k(20)个簇的信息。 [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 2 3 4 5 </td><td width="623">[color=#B85C00 !important]# Get to Local [color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]get [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#004ED0 !important]hadoop [color=#002D7A !important]dfs [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]get [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#B85C00 !important]# View .. [color=#004ED0 !important]mahout [color=#002D7A !important]clusterdump [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]i [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusters [color=#006FE0 !important]- [color=#CE0000 !important]2 [color=#006FE0 !important]- [color=#800080 !important]final [color=#006FE0 !important][color=#006FE0 !important]- d [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]sparse [color=#006FE0 !important]/ [color=#002D7A !important]dictionary [color=#333333 !important]. [color=#002D7A !important]file [color=#006FE0 !important]- [color=#CE0000 !important]0 [color=#006FE0 !important][color=#006FE0 !important]- [color=#004ED0 !important]dt [color=#002D7A !important]sequencefile [color=#006FE0 !important][color=#006FE0 !important]- o [color=#006FE0 !important][color=#333333 !important]. [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]- [color=#002D7A !important]cluster [color=#006FE0 !important]- [color=#002D7A !important]dump [color=#006FE0 !important]/ [color=#006FE0 !important][color=#006FE0 !important]- n [color=#006FE0 !important][color=#CE0000 !important]20 </td></tr></table>要说明的是，clusterdump似乎只能在本地执行……所以先把数据下载到本地吧。 参数说明： <ul> <li>-i ：我们只看最终迭代生成的簇结果。 <li>-d ：使用词 -> 词id 映射，使得我们输出结果中，可以直接显示每个簇，权重最高的词文本，而不是词id。 <li>-dt：上面映射类型，由于我们是seqdictionary生成的，so。。 <li>-o：最终产出目录 <li>-n：每个簇，只输出20个权重最高的词。 </ul>看看dump结果吧： 一共有20行，表示20个簇。每行形如： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 </td><td width="623">[color=#004ED0 !important]VL [color=#006FE0 !important]- [color=#CE0000 !important]12722 [color=#333333 !important]{ [color=#002D7A !important]n [color=#006FE0 !important]= [color=#CE0000 !important]1305 [color=#006FE0 !important][color=#002D7A !important]c [color=#006FE0 !important]= [color=#333333 !important][ [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. zorinsky' [color=#002D7A !important]s [color=#006FE0 !important]: [color=#CE0000 !important]0.011 [color=#333333 !important], [color=#006FE0 !important][color=#002D7A !important]zurich [color=#006FE0 !important]: [color=#CE0000 !important]0.006... [color=#333333 !important]] [color=#333333 !important], [color=#006FE0 !important][color=#002D7A !important]r [color=#006FE0 !important]= [color=#333333 !important][ [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. [color=#002D7A !important]yuan [color=#006FE0 !important]: [color=#CE0000 !important]1.055 [color=#333333 !important], [color=#006FE0 !important][color=#002D7A !important]yugoslav [color=#006FE0 !important]: [color=#CE0000 !important]1.027 [color=#333333 !important], [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]] [color=#333333 !important]} [color=#006FE0 !important] [color=#004ED0 !important]Top [color=#002D7A !important]Terms [color=#006FE0 !important]: [color=#006FE0 !important] [color=#002D7A !important]he [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]3.105303428364896 [color=#006FE0 !important] [color=#002D7A !important]said [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]2.8756448350190205 [color=#006FE0 !important] [color=#002D7A !important]would [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]2.6413800148214874 [color=#006FE0 !important] [color=#002D7A !important]have [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]2.1552908992401942 [color=#006FE0 !important] [color=#002D7A !important]government [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.8426488105364687 [color=#006FE0 !important] [color=#002D7A !important]which [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.749669294978467 [color=#006FE0 !important] [color=#002D7A !important]economic [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.7431561736768233 [color=#006FE0 !important] [color=#002D7A !important]has [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.7429241635333532 [color=#006FE0 !important] [color=#002D7A !important]prices [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.7182022383386604 [color=#006FE0 !important] [color=#002D7A !important]oil [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.673632335845538 [color=#006FE0 !important] [color=#002D7A !important]from [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.64287882106971 [color=#006FE0 !important] [color=#002D7A !important]u [color=#333333 !important]. [color=#002D7A !important]s [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.6223870217115028 [color=#006FE0 !important] [color=#002D7A !important]had [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.602064758607711 [color=#006FE0 !important] [color=#002D7A !important]more [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.5874425666999086 [color=#006FE0 !important] [color=#002D7A !important]last [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.561653600890061 [color=#006FE0 !important] [color=#002D7A !important]we [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.5274837373316974 [color=#006FE0 !important] [color=#002D7A !important]been [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.4653439554674872 [color=#006FE0 !important] [color=#002D7A !important]year [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.4279387724353894 [color=#006FE0 !important] [color=#002D7A !important]could [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.4152588548331426 [color=#006FE0 !important] [color=#002D7A !important]minister [color=#006FE0 !important] [color=#006FE0 !important]= [color=#006FE0 !important]> [color=#006FE0 !important] [color=#CE0000 !important]1.4146991936183066 </td></tr></table>其中前面的12722是簇的ID，n=1305即簇中有这么多个文档。c向量是簇中心点向量，格式为词文本:权重(点坐标)，r是簇的半径向量，格式为词文本:半径。 下面的Top Terms是簇中选取出来的特征词。 3、查看聚类结果 其实，聚类结果中，更重要的是，文档被聚到了哪个类。 遗憾的是，在很多资料中，都没有说明这一点。前文我们已经提到了，簇id -> 文档id的结果，保存在了clusteredPoints下面。这也是mahout内置类型存储的。我们可以用seqdumper命令查看。 [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#004ED0 !important]mahout [color=#002D7A !important]seqdumper [color=#006FE0 !important][color=#006FE0 !important]- [color=#002D7A !important]i [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]user [color=#006FE0 !important]/ [color=#002D7A !important]coder4 [color=#006FE0 !important]/ [color=#002D7A !important]reuters [color=#006FE0 !important]- [color=#002D7A !important]kmeans [color=#006FE0 !important]/ [color=#002D7A !important]clusteredPoints [color=#006FE0 !important]/ </td></tr></table>其中，-d和-dt的原因同clusterdump。 如果不指定-o，默认输出到屏幕，输出结果为形如： [color=#333333 !important] <table cellspacing="0" class="t_table" ><tr><td>1 </td><td width="623">[color=#002D7A !important]Key [color=#006FE0 !important]: [color=#006FE0 !important][color=#CE0000 !important]4255 [color=#006FE0 !important]: [color=#006FE0 !important][color=#002D7A !important]Value [color=#006FE0 !important]: [color=#006FE0 !important][color=#002D7A !important]wt [color=#006FE0 !important]: [color=#006FE0 !important][color=#CE0000 !important]1.0 [color=#006FE0 !important][color=#002D7A !important]distance [color=#006FE0 !important]: [color=#006FE0 !important][color=#CE0000 !important]0.7752480913348985 [color=#006FE0 !important] [color=#002D7A !important]vec [color=#006FE0 !important]: [color=#006FE0 !important][color=#006FE0 !important]/ [color=#002D7A !important]reut2 [color=#006FE0 !important]- [color=#CE0000 !important]000.sgm [color=#006FE0 !important]- [color=#CE0000 !important]0.txt [color=#006FE0 !important][color=#006FE0 !important]= [color=#006FE0 !important][color=#333333 !important][ [color=#CE0000 !important]14 [color=#006FE0 !important]: [color=#CE0000 !important]4.670 [color=#333333 !important], [color=#006FE0 !important][color=#CE0000 !important]35 [color=#006FE0 !important]: [color=#CE0000 !important]7.545 [color=#333333 !important], [color=#006FE0 !important][color=#333333 !important]. [color=#333333 !important]. [color=#333333 !important]. [color=#006FE0 !important][color=#CE0000 !important]11278 [color=#006FE0 !important]: [color=#CE0000 !important]6.394 [color=#333333 !important], [color=#006FE0 !important][color=#CE0000 !important]11288 [color=#006FE0 !important]: [color=#CE0000 !important]6.731 [color=#333333 !important]] </td></tr></table>其实，这个输出是一个SequenceFile，大家自己写程序也可以读出来的。 Key是ClusterID，上面clusterdump的时候，已经说了。 Value是文档的聚类结果：wt是文档属于簇的概率，对于kmeans总是1.0，/reut2-000.sgm-0.txt就是文档标志啦，前面seqdirectionary的-nv起作用了，再后面的就是这个点的各个词id和权重了。 三、Fuzzy-KMeans KMeans是一种简单有效的聚类方法，但存在一些缺点。 例如：一个点只能属于一个簇，这种叫做硬聚类。而很多情况下，软聚类才是科学的。例如：《哈利波》属于小说，也属于电影。Fuzzy-Kmeans 通过引入“隶属度”的方式，实现了软聚类。   <div class="best_aws" style="margin-top: 10px;"> </div> </div> </div> <div id="post_rate_div_8561144"></div>  </td> </tr> <tr> <td class="plc plm" style="padding-bottom:0px"> </td> </tr> <tr id="_postposition8561144"></tr> <tr> <td class="plc"> <div style="height: 36px;"> <div class="pob" style="position: relative;"> 2021-9-23 10:13:22 <a class="show-replay" 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var content = '<div class="downtip"> <div class="downtip-hd">抱歉！您的VIP已失效！</div> <div class="downtip-bd"> 开通VIP会员,立享全站资料免积分下载</div><div class="downtip-ft"><a href="//www.hzfubeitong.com/bbs/vip/" onclick="hideMenu('+"'fwin_dialog'"+', '+"'dialog'"+')" id="needvip" target="_blank">立即开通</a></div></div>'; setTimeout(function(){ $(".vip-down"+i).click(function(){ showDialog(content,'info'); }); },500); }else{ $(this).after('<a class="vip-home" target="_blank" href="//www.hzfubeitong.com/bbs/vip/">开通VIP会员，免积分下载>></a>') } } } } }); } }); } //点击加入小组直接调用接口然后刷新页面 $("body").on("click",'.only_group_ajax',function(){ var attr_uid=$(this).attr("attr_uid"); var attr_href=$(this).attr("attr_href"); if(!attr_uid || attr_uid==0){ window.location.href=attr_href; }else{ $.ajax({ url: '/forum.php?mod=group&action=join&ajaxdata=json&fid='+'999', type: 'post', dataType: 'json', success: function (res) { // if(res.message==="您已加入该小组"){ // window.location.reload() // } var layer_my='<div id="my_message_pop" style="z-index:999999;position:fixed;top:50%;left:50%;transform:translate(-50%,-50%);background-color: rgba(0,0,0,0.8);color: #fff;padding: 10px 30px;border-radius: 5px;" >' +'加入成功'+ '</div>'; $("body").append(layer_my); var setTimer=setTimeout(function(){ $("body #my_message_pop").remove() clearTimeout(setTimer) window.location.reload() },1000) } }) } }) //相关文章 var thread_subject_page=1 $("body").on("click",".tab_relate_btn span",function(){ var span_index= $(this).index() var wd_count=$(".tab_relate_btn").attr("wd_count") if(span_index==0){ if(thread_subject_page==1){ }else{ thread_subject_page-- } }else{ if(thread_subject_page==wd_count){ }else{ thread_subject_page++ } } if(!$(this).hasClass("disabled")){ //问答 var title=$("#thread_subject").html(); if(3 ==3){ special3('/app/api/index.php?s=Home/es/ask&title='+title+'&page='+thread_subject_page ,title,thread_subject_page) // 资料 }else if(3 ==8){ special3('/app/api/index.php?s=Home/es/down&title='+title+'&page='+thread_subject_page ,title,thread_subject_page) }else { special3('/app/api/index.php?s=Home/es/other&title='+title+'&page='+thread_subject_page ,title,thread_subject_page) } } }) function special3(url,title,page){ //问答 $.ajax({ url: url, type: 'post', dataType: 'json', success: function (res) { var data=res.data; var html=''; var img='' for(var k in data){ if(data[k].type==1){ img='//www.hzfubeitong.com/bbs/template/elecfans_201805/images/u59.png' }else{ img='template/elecfans_201805/images/zl_icon/d_load.png' } html+= '<li><a href="'+data[k].url+'" title="'+data[k].subject+'" target="_blank">• '+data[k].subject+'<img class="exViewImg" style="width: 14px;" src="'+img+'">'+data[k].views+'</a></li>' } $(".newpg-list ul").html(html) if(page==res.page_total){ $(".tab_relate_btn span").eq(0).removeClass("disabled") $(".tab_relate_btn span").eq(1).addClass("disabled") }else if(page==1){ $(".tab_relate_btn span").eq(0).addClass("disabled") $(".tab_relate_btn span").eq(1).removeClass("disabled") }else{ $(".tab_relate_btn span").eq(0).removeClass("disabled") $(".tab_relate_btn span").eq(1).removeClass("disabled") } } }) } renderViewNum() //动态渲染浏览量 function renderViewNum(){ $.ajax({ url:'/webapi/Home/Live/getViewsById', type:'get', data:{ id: 2168577 }, success:function(res){ if(res.status == 'successed' ){ var views = res.data.views; if($(".glance").length>0){ $(".glance").html('<img style="float: left;margin-top: 16px;" src="//www.hzfubeitong.com/bbs/template/elecfans_201805/images/zl_icon/wacth.png">'+views) } } } }) } var authorId = $('.authorInfoBox').attr('data-authorid'); $.ajax({ url:'/forum.php?mod=ajax&action=ask_usercard&ajaxdata=json&uid='+authorId, dataType:'json', success:function(data){ /*获取用户参数*/ var getData = data.data.userdata; var isFollow = getData.is_follow, avatar = getData.avatar, company = getData.company, position = getData.position, _uid = getData.uid, userLink = '/user/'+_uid, threads = getData.threads, extcredits = getData.extcredits3, credits = getData.credits, articles = getData.articles, follower = getData.follower, grouptitle = getData.grouptitle, is_vip = getData.is_vip, username = getData.username; var getUid = _uid; var talkUrl='/home.php?mod=spacecp&ac=pm&op=showmsg&handlekey=showmsg_'+getUid+'&touid='+getUid+'&pmid=0&daterange=2'; /*判断是否已关注*/ if(isFollow>0){ var attenTxt = '<div data-id="'+getUid+'" class="attend remove-follow lf">已关注</div>' }else{ var attenTxt = '<div class="attend creat-follow lf" data-id="'+getUid+'">+关注</div>' } /*判断公司名和职位显示*/ if(position && company){ var peoMsg = ''+position+' '+company+''; }else{ var peoMsg = ''+position+company+''; } if(is_vip > 0){ var vipLogo = '<img src="//www.hzfubeitong.com/staticbbs/template/elecfans_201805/images/vip_icon.png" style="position: absolute;top: 21px; width:16px">' } else { var vipLogo = "" } var usermedals = '' for(var index in getData.usermedals){ usermedals += getData.usermedals[index] } $('.authorInfoBox').html('<div class="avatar">'+ '<a href="'+userLink+'" target="_blank"><img src="'+avatar+'"></a>'+ '</div>'+ '<a href="'+userLink+'" target="_blank" class="xw1" style="position:relative; font-size: 14px; color: #333;">'+username+'</a> '+vipLogo+''+ '<a target="_blank" class="xw1 popUserName">'+peoMsg +'</a>'+ ''+ ''+grouptitle+" "+usermedals+''+ '积分：'+extcredits+'经验：'+credits+''+ '<div class="user_msg_num" style="">'+ '<ul>'+ '<li>'+ '主题'+ '<h5 style="font-size:12px; color: #333; font-weight: bold; ">'+threads+'</h5>'+ '</li>'+ '<li>'+ '文章'+ '<h5 style="font-size:12px; color: #333; font-weight: bold;">'+articles+'</h5>'+ '</li>'+ '<li>'+ '粉丝'+ '<h5 style="font-size:12px; color: #333; font-weight: bold;">'+follower+'</h5>'+ '</li>'+ '</ul>'+ '</div>'+ '<div class="user_msg_btn">'+ ( authorId != uid ? attenTxt+'<a onclick="showWindow(\'showMsgBox\','+"'"+talkUrl+"'"+',\'get\', 0)">发私信</a>' : "" )+ '</div>') } }); })(jQuery); </script> <script type="text/javascript"> // 根据Uid判断是否登录 let isLogin = 0; if(!isLogin) { var attachNode = document.querySelectorAll(".attachment"); Array.prototype.forEach.call(attachNode, function (link) { // 不显示a链接 link.setAttribute("href","javascript:;") link.addEventListener('click', function (e) { showWindow('login', 'member.php?mod=logging&action=login') }) }) } </script> <script src="//www.hzfubeitong.com/staticbbs/static/js/share-web.js?Rzv" type="text/javascript"></script>

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