mahout从文本建造向量

Introduction For clustering documents it is usually necessary to convert the raw text into vectors that can then be consumed by the clustering Algorithms . These approaches are described below.
From Lucene Mahout has utilities that allow one to easily produce Mahout Vector representations from a Lucene (and Solr, since they are they same) index.
For this, we assume you know how to build a Lucene/Solr index. For those who don't, it is probably easiest to get up and running using Solr as it can ingest things like PDFs, XML, Office, etc. and create a Lucene index. For those wanting to use just Lucene, see the Lucene website or check out Lucene In Action by Erik Hatcher, Otis Gospodnetic and Mike McCandless.
To get started, make sure you get a fresh copy of Mahout from SVN and are comfortable building it. It defines interfaces and implementations for efficiently iterating over a Data Source (it only supports Lucene currently, but should be extensible to databases, Solr, etc.) and produces a Mahout Vector file and term dictionary which can then be used for clustering. The main code for driving this is the Driver program located in the org.apache.mahout.utils.vectors package. The Driver program offers several input options, which can be displayed by specifying the --help option. Examples of running the Driver are included below:
Generating an output file from a Lucene Index $MAHOUT_HOME/bin/mahout lucene.vector <PATH TO DIRECTORY CONTAINING LUCENE INDEX> \   --output <PATH TO OUTPUT LOCATION> --field <NAME OF FIELD IN INDEX> --dictOut <PATH TO FILE TO OUTPUT THE DICTIONARY TO] \   <--max <Number of vectors to output>> <--norm {INF|integer >= 0}> <--idField <Name of the idField in the Lucene index>>

Create 50 Vectors from an Index $MAHOUT_HOME/bin/mahout lucene.vector --dir <PATH>/wikipedia/solr/data/index --field body \    --dictOut <PATH>/solr/wikipedia/dict.txt --output <PATH>/solr/wikipedia/out.txt --max 50

This uses the index specified by --dir and the body field in it and writes out the info to the output dir and the dictionary to dict.txt. It only outputs 50 vectors. If you don't specify --max, then all the documents in the index are output.
Normalize 50 Vectors from a Lucene Index using the L_2 Norm $MAHOUT_HOME/bin/mahout lucene.vector --dir <PATH>/wikipedia/solr/data/index --field body \      --dictOut <PATH>/solr/wikipedia/dict.txt --output <PATH>/solr/wikipedia/out.txt --max 50 --norm 2

From Directory of Text documents Mahout has utilities to generate Vectors from a directory of text documents. Before creating the vectors, you need to convert the documents to SequenceFile format. SequenceFile is a hadoop class which allows us to write arbitary key,value pairs into it. The DocumentVectorizer requires the key to be a Text with a unique document id, and value to be the Text content in UTF-8 format.
You may find Tika (http://lucene.apache.org/tika ) helpful in converting binary documents to text.
Converting directory of documents to SequenceFile format Mahout has a nifty utility which reads a directory path including its sub-directories and creates the SequenceFile in a chunked manner for us. the document id generated is <PREFIX><RELATIVE PATH FROM PARENT>/document.txt
From the examples directory run
$MAHOUT_HOME/bin/mahout seqdirectory \--input <PARENT DIR WHERE DOCS ARE LOCATED> --output <OUTPUT DIRECTORY> \<-c <CHARSET NAME OF THE INPUT DOCUMENTS> {UTF-8|cp1252|ascii...}> \<-chunk <MAX SIZE OF EACH CHUNK in Megabytes> 64> \<-prefix <PREFIX TO ADD TO THE DOCUMENT ID>>

Creating Vectors from SequenceFile Mahout_0.3
From the sequence file generated from the above step run the following to generate vectors.
$MAHOUT_HOME/bin/mahout seq2sparse \-i <PATH TO THE SEQUENCEFILES> -o <OUTPUT DIRECTORY WHERE VECTORS AND DICTIONARY IS GENERATED> \<-wt <WEIGHTING METHOD USED> {tf|tfidf}> \<-chunk <MAX SIZE OF DICTIONARY CHUNK IN MB TO KEEP IN MEMORY> 100> \<-a <NAME OF THE LUCENE ANALYZER TO TOKENIZE THE DOCUMENT> org.apache.lucene.analysis.standard.StandardAnalyzer> \<--minSupport <MINIMUM SUPPORT> 2> \<--minDF <MINIMUM DOCUMENT FREQUENCY> 1> \<--maxDFPercent <MAX PERCENTAGE OF DOCS FOR DF. VALUE BETWEEN 0-100> 99> \<--norm <REFER TO L_2 NORM ABOVE>{INF|integer >= 0}>"<-seq <Create SequentialAccessVectors>{false|true required for running some algorithms(LDA,Lanczos)}>"

--minSupport is the min frequency for the word to be considered as a feature. --minDF is the min number of documents the word needs to be in
--maxDFPercent is the max value of the expression (document frequency of a word/total number of document) to be considered as good feature to be in the document. This helps remove high frequency features like stop words
Background

• http://www.lucidimagination.com/search/document/3d8310376b6cdf6b/centroid_calculations_with_sparse_vectors#86a54dae9052d68c
• http://www.lucidimagination.com/search/document/4a0e528982b2dac3/document_clustering
  

From a Database TODO:
Other Converting existing vectors to Mahout's format If you are in the happy position to already own a document (as in: texts, images or whatever item you wish to treat) processing pipeline, the question arises of how to convert the vectors into the Mahout vector format. Probably the easiest way to go would be to implement your own Iterable<Vector> (called VectorIterable in the example below) and then reuse the existing VectorWriter classes:
VectorWriter vectorWriter = SequenceFile.createWriter(filesystem, configuration, outfile, LongWritable.class, SparseVector.class);long numDocs = vectorWriter.write(new VectorIterable(), Long.MAX_VALUE);https://cwiki.apache.org/confluence/display/MAHOUT/Creating+Vectors+from+Text